System for neurobehaviorual animation

ABSTRACT

The present invention relates to a computer implemented system for animating a virtual object or digital entity. It has particular relevance to animation using biologically based models, or behavioural models particularly neurobehavioural models. There is provided a plurality of modules having a computational element and a graphical element. The modules are arranged in a required structure and have at least one variable and being associated with at least one connector. The connectors link variables between modules across the structure, and the modules together provide a neurobehavioural model. There is also provided a method of controlling a digital entity in response to an external stimulus.

This application is a Continuation of U.S. application Ser. No.14/909,570 filed 2 Feb. 2016, which is a National Stage Application ofPCT/NZ2014/000156, filed 4 Aug. 2014, which claims benefit of U.S.Application No. 62/005,195, filed 30 May 2014 and which applications arehereby incorporated by reference in their entireties. To the extentappropriate, a claim of priority is made to each of the above disclosedapplications.

FIELD OF THE INVENTION

The present invention relates to a system and method of simulating avirtual object or digital entity capable of animation. The invention hasparticular application to a method and system for animation usingbiologically based models, or behavioural models, particularlyneurobehavioural models.

BACKGROUND OF INVENTION

As animation and digital technology have moved forward the interface orinteraction between a human user and a computer or digital entity hasdeveloped significantly. A human-like machine or computer system able toprocess information intelligently, interact and present itself in ahuman-like manner is desirable. This is in part because human usersinteract better with human-like systems and/or robots. Secondly a morehuman-like system may have more realistic actions, responses andanimations, thus reducing perceived technology barriers including theuncanny valley effect.

Animations of this type present a number of significant technicalproblems. Firstly, the human-like or animal-like function needs to bemodelled, which in itself is extremely challenging. Then there is thechallenge of taking the human-like function and using it to create avisual or graphical response that is believable to a user or viewer. Oneexample of a difficult response is facial expression. If the system isone which interacts with a user i.e. is interactive, then there is theadditional challenge of processing visual and/or audio input data.

These challenges present technical problems. The human-like models needto be integrated with graphics, animation and sensors in such a way thatthe system is flexible (it may need to be changed depending on therequired application) and usable by a programmer/developer (the systemsshould be relatively intuitive or at least capable of being generallyunderstood by a programmer) while also being able to be compiled and runefficiently.

Existing systems do not adequately address these problems. Some knownsystems are discussed below.

Animation Type Programs

The controls systems and signal processing fields have produced visualprogramming languages such as Simulink™ and VisSim™. The use of thesevisual systems has broadened into other fields as the systems provide aneffective way to create a system and have programming code automaticallygenerated. In a typical example a Simulink system may be built byconnecting a series of block units (the block units representing forexample an electrical component or group of electrical components) so asto link inputs and outputs as desired. This system is then compiled byevaluating the block structure and system attributes, reconstructing themodel in a flattened structure, and ordering the block operations. Inthis sense the visual design is being used to create an understandableview of the model. However the model is operating in an ordered andcentralised manner. Similar visual type programs are also known to makecoding or circuit arrangement more straightforward.

Animation and 3D drawing programs are also known, for example AutodeskMaya™ uses node graph architecture to represent complex 3D graphics.Autodesk Maya allows animations to be produced and structured overmultiple different levels. Instructions may then be supplied to theanimation to encourage interaction with an environment. Some programsinterface between animation and functional aspects including Max™ visualprogramming using Jitter. In these cases the graphics engine issubstantially separate from, but controlled by, some other program ormeans (such as sound for Jitter). In other cases the complexity ofanimation simulations is overcome through the use of a limited set ofpossible actions. For example Havok Animation Studio™ (HAS) providesefficient character animation through the use of finite state machines(FSM). With the university of Southern California's (USCs) institute forcreative technologies' (ICTs) Virtual Human toolkit, Cerebella,automatic generation of animated physical behaviours can be generatedbase upon accompanying dialogue however the Cerebella requires the inputof detailed information about a character's mental state to create asuitable animation.

Neural Models Systems

Neural network based models, including programs such as SNNS andEmergent provide a variety of neural network environments. In differentprograms the models may provide biological type neurons or may buildartificial neural networks. An effective neural network may contain manyhundreds or thousands of neurons to simulate even straightforwardmodels. The complexity in using large neural networks led to attempts tobuild artificial intelligence (AI) based devices. Social or personalrobots, such as those developed by MIT Leonardo, appear to havehuman-like qualities. However they must be programmed in rigid andinflexible manners, typically they require specific implementation ofpossible actions and are dependent on certain hardware or inflexible.

Artificial Intelligent Robots

Neuro-robotic and/or brain based devices attempt to produce human likesystems by copying brain based functions to create desired interactions.These models are typically very large, replicating complete brainsystems from low level neurons and linking systems with biological-likeinterface systems. Brain based devices are robots built to emulatebehaviour generated by nervous systems. These typically attempt to havehuman-like actions and an array of sensors but do not provide aninteractive experience through interaction with humans. Brain baseddevices are designed for particular robots or applications and typicallylack broad support for a range of different operations.

In summary known systems do not have the ability to adequately performone or more of the following:

-   -   accommodate multiple models having different levels of        simulation detail;    -   perform high level and low level simulations;    -   integrate and prioritise animation and graphics as part of the        simulation;    -   provide visual or animated outputs of multiple models that may        together comprise the simulated system;    -   provide an environment which has the required flexibility to        adjust, remove or replicate model components;    -   provide an environment which is readily understandable to a        modeller or developer    -   provide an animation system based on biological neural systems.    -   provide learning abilities

OBJECTS OF THE INVENTION

It is an object of the present invention to provide a computerimplemented system or method for simulating a virtual object which maybe able to overcome or at least ameliorate one or more of the aboveproblems, or which will at least provide a useful alternative.

It is a further object of the present invention to provide a computerimplemented system or method for providing an animated real or virtualobject or digital entity based on a neurobehavioural model.

It is a further object of the present invention to provide a computerimplemented system or method for describing a digital entity based on aneurobehavioural model.

It is a further object of the present invention to provide an avatarwith increased complexity, detail, richness or responsiveness tostimulus and a system for controlling avatars which is interactive andallows adjustment of the characteristics of interaction.

Further objects of the invention will become apparent from the followingdescription.

BRIEF SUMMARY OF THE INVENTION

In one aspect the invention addresses the technical problem of flexiblyintegrating a real-world input stimulus with virtual neuro-behaviouralmodels or model components in a machine so that the machine provides aninteractive animation.

In another aspect the invention addresses the technical problem of usinga machine to integrate individual neural or neurobehavioural models ofdifferent scale.

In another aspect the invention addresses the technical problem ofallowing relationships between model components to be varied or changedso that a programmer may use the machine to easily identify andimplement changes in the overall neuro-behavioural model, or changes inthe animation or an interactive aspect of the animation.

In a first aspect the invention may broadly provide a computerimplemented system for animating a virtual object or digital entity, thesystem including a plurality of modules having a computational elementand a graphical element,

the modules being arranged in a required structure,

each module having at least one variable and being associated with atleast one connector, wherein the connectors link variables betweenmodules across the structure, and the modules together provide aneurobehavioural model.

In one embodiment the modules are arranged in a structure such as ahierarchical structure. In one embodiment the hierarchy comprises a treestructure.

In one embodiment the structure is derived from a biological property ofbiological structure of the animated object.

In one embodiment the structure is derived from an evolutionary neuralstructure.

The hierarchy may be a tree structure, and may be dependent on aproperty of the animated object. For example, the hierarchical structuremay be derived from biological properties or structure present in, orrequired by, the animated object. Thus if the object is a human face,the structure may include a hierarchy in which a module includingcomputational and graphical features relating to the cornea is dependentfrom (hierarchically inferior to) a module relating to the eye.

The hierarchical structure may additionally or alternatively relate toevolutionary properties or structure of the simulated object, forexample evolutionary brain or neural structure.

The use of a tree structure facilitates identification of modulefunction within the context of the simulated object.

The use of connectors provides significant flexibility, and allowsvariables to be linked across multiple modules creating the linksbetween modules that form a complex neurobehavioural model. Theconnectors also assist when reducing repetition of model features andprovide greater efficiency in modelling systems and in the operation ofsystems as they clearly indicate how the system is linked.

In one embodiment the system comprises at least one module includes anaudial or graphical or visual input or stimuli and at least one modulehaving an audial or graphical or visual output.

In one embodiment a portion of the system is representative of a brain.

In one embodiment the graphical element of each module may be toggledbetween visible and hidden.

In one embodiment a module may have more than one possible graphicalelement.

In one embodiment the graphical element of one or more modules comprisesa representation of the computational element.

The graphical element may provide in module support for CPUs, shadersand other graphical tools so as there are straightforward means tocreate a graphical output for each or any of the modules. For instance amodule's neuron activity could be connected to a colour, audial orvisual output without having to create a new module.

In one embodiment a module represents one or more neurons.

In one embodiment a module may represent a biological model.

In one embodiment at least one of the modules may represent a high levelsystem and at least one of the modules may represent a low-level system.

In one embodiment variables from a module may be linked to any of theplurality of modules by a connector.

In one embodiment the modules may have additional modules related to itthrough the required structure which perform a portion of the modulesoperation.

In one embodiment at least one of the modules is an association modulewhich links inputs and outputs of the module through variable weights.

In one embodiment the association module has fixed weights.

In one embodiment the graphical element of a module may be switched onor off.

In one embodiment a module may have multiple graphical elements, eachelement having a separate graphical output.

In one embodiment the required structure establishes the relationshipbetween modules.

In one embodiment the plurality of modules may have a transformationelement.

In one embodiment the transformation element adapts the graphical outputof the module based on modules linked by the required structure.

In one embodiment at least one of the plurality of modules has agraphical input.

In one embodiment the system has at least one graphical output.

In one embodiment one of the plurality of modules produces a graphicaloutput of a linked variable.

In one embodiment one of the plurality of modules has an input from anexternal stimulus/stimuli.

In one embodiment the system is capable of learning from an externalstimulus/i.

In one embodiment the system provides stimuli externally.

In one embodiment the system is interactive with a user or environment.

In one embodiment one or more of the modules has a learning or memoryelement,

In one embodiment the learning element is implemented by an associationelement.

In one embodiment the association element is a synapse weights module.

In one embodiment the operation of a module is modulated by a modulatingvalue connected to the module.

In one embodiment the modulating value is related to aneurotransmitter/neuromodulator.

In one embodiment each module performs an action when the model istime-stepped.

In one embodiment the object may be a virtual object.

In one embodiment the connectors may communicate using a standardisednetwork format.

In one embodiment the connectors may communicate time-varying data.

In one embodiment the connectors may introduce timing and/or delayattributes.

In one embodiment the timing and/or delay elements may depend on aproperty of the connection or structure.

In another aspect the invention may broadly provide a computerimplemented system for animating an object or digital entity, the systemincluding a plurality of modules having a computational element and agraphical element,

-   -   each computational element having a module type and at least one        variable, and being associated with at least one connector,    -   wherein the connectors link variables between modules and the        linked modules together are representative of a graphical and        computational model of the animated virtual object.

In one embodiment the system comprises an input for receiving an audialor visual input stimulus.

In an embodiment the invention may comprise a sensing element.

In another aspect the invention may broadly provide a computerimplemented system for animating an object, the system including aplurality of modules,

-   -   each module having a type selected from an interface type, an        animation type and a neuron type,    -   each module having a variable, and being associated with a        connector,    -   wherein the connectors link variables between modules and the        linked modules together are representative of a graphical and        computational model of the animated object.

In an embodiment each module may be selected from a plurality ofpre-defined modules.

In an embodiment the system may comprise an input module which is aninterface type and an output module which is an animation type module.

In an embodiment the system may include one or a plurality of learningmodules.

In an embodiment the inputs and/or outputs may include graphical orcomputational information.

In an embodiment the modules are arranged to mimic a biologicalstructure.

In an embodiment the model is a neurobehavioural model.

In yet another aspect a method of programming an animation is provided,the method comprising the steps of:

-   -   creating a required structure of modules, each module associated        with a portion of the animation and able to comprise a        computation element, a graphic element, a transformation        element, and a set of inputs and/or outputs, wherein the        computation and graphic elements are associated with the portion        of the animation,    -   creating a plurality of connections between a plurality of the        modules, the connections occurring between the inputs and        outputs of each module,    -   wherein the hierarchy of modules and the plurality of        connections define an animated system and the model controls the        animated system.

In an embodiment the required structure is a hierarchy.

In an embodiment the inputs and/or outputs are variables of the modules.

In an embodiment the hierarchy and/or connections may replicateneurobehavioural systems.

In an embodiment the hierarchy and/or connections may replicate neuralcircuits.

In an embodiment the method may comprise the further step of varying theconnections between elements to vary the animation.

In an embodiment the method one or more of the modules may be learningmodules.

In an embodiment the method may comprise the further step of allowing alearning module to adapt based on the set of inputs and/or outputs.

In an embodiment the plasticity of the learning module may be altered.

In an embodiment the method comprises the step of selecting each of themodules from a plurality of predefined modules or module types.

In an embodiment the method comprises the step of adjusting a predefinedmodule to provide a desired operation.

In an embodiment one or more of the required structure of modules is alearning module.

In an embodiment the method comprises the step of allowing a learningmodule to adapt based on input data then fixing the operation of alearning module.

In another aspect the invention may broadly provide a computerimplemented method of animating an object or digital entity, the methodincluding the steps of:

-   -   providing a plurality of modules which together simulate a        neurobehavioural model, a plurality of the modules each having a        graphical element, and    -   processing the modules such that a transformation of an        anatomical feature of the object or entity results in        corresponding transformation of one or more sub-parts of that        anatomical feature.

In another aspect the invention may broadly provide a computerimplemented method of animating an object or digital entity, the methodincluding the steps of:

-   -   providing a plurality of modules which together provide a        neurobehavioural model, a plurality of the modules each having a        graphical element,    -   processing the modules in a time stepped manner to provide        graphical information for each module in each time step,    -   evaluating a real time constraint, and    -   rendering the graphical information if the real time constraint        is satisfied.

In an embodiment the rendering of the graphical information may occurafter a plurality of time-steps have been processed.

In another aspect the invention may broadly provide a computerimplemented system for animating an object or digital entity, the systemincluding a plurality of modules capable of having a computationalelement, a graphical element and one or more variables,

-   -   wherein at least one of the plurality of modules creates a        graphical output feature,    -   at least one of the plurality of modules is adapted to change        the appearance of the graphical output feature, and    -   at least one of the plurality of modules is an association        module which comprises weights to link input and output        variables.

In an embodiment at least one of the plurality of modules is a learningmodule adapted to alter the future actions of the animated virtualobject or digital entity.

In an embodiment the association module is a learning module.

In an embodiment the plasticity of the learning module is adjustable tocontrol the rate of learning.

In an embodiment at least one of the plurality of modules is a learningmodule in which the learning has been stopped.

In an embodiment the association module has inputs from one or moremodules forming a neurobehavioural model and outputs to one or moremodules forming a graphical output.

In an embodiment the association module weights are fixed.

In an embodiment the association weights are fixed based on externaldata.

In an embodiment the association module weights represent a graphicaloutput.

In an embodiment each of a plurality of the plurality of modules areassociation modules represent alternative graphical outputs.

In an embodiment each of the alternative graphical outputs may bedisplayed separately or may be displayed in a blended combination.

In an embodiment the graphical output may represent a face.

In an embodiment the alternative graphical outputs may represent a rangeof facial expressions. In an embodiment the graphical output is apositioning signal to one or more of a plurality of graphical outputcomponents.

In an embodiment the graphical output components represent muscles.

In a further aspect the invention may be broadly described as a computergame, having one or more characters as described in the other aspects.

In a further aspect the invention may be broadly described as aninteractive display showing a virtual object or digital entity asdescribe in the other aspects.

In an embodiment the interactive display may be an advertising display.

In another aspect the invention may broadly provide a computerimplemented system for generating interactive behaviour, the systemincluding a plurality of modules having a computational element and agraphical element,

the modules being arranged in a required structure,

each module having at least one variable and being associated with atleast one connector, wherein the connectors link variables betweenmodules across the structure, and the modules together provide abehavioural or neurobehavioural model.

In another aspect the invention may broadly provide a computerimplemented system for generating interactive behaviour, the systemincluding a plurality of modules having a computational element and agraphical element,

-   -   at least one of the plurality of modules receiving an external        stimulus,    -   at least one of the plurality of modules providing an external        output,    -   at least one of the plurality of modules creating an association        between the external stimulus and the external output,    -   wherein the association affects future system behaviour such        that the external output responds a change in the external        stimulus.

In an embodiment the association provides the system with a learningbehaviour.

In an embodiment at least one of the modules creates an associationbetween a first internal stimulus and a second internal stimulus or theexternal output.

In an embodiment at least one of plurality of modules has a modulatingmeans to modulate the function of one of the plurality of modules.

In another aspect the invention may broadly provide a computerimplemented method of animating a virtual object or digital entity, themethod including the steps of:

-   -   Instantiating a plurality of modules from a plurality of module        templates,    -   Defining, for the plurality of modules a function, input and        output,    -   Defining connections between the inputs and outputs of the        plurality of modules, wherein the plurality of modules and        connections form a behavioural or neurobehavioural model.

In an embodiment at least one of the inputs and/or outputs to at leastone of the plurality of modules is an external stimuli or output.

In an embodiment any one or more of the plurality of modules orconnections may have a visualisation output.

In another aspect the invention may broadly provide a computerimplemented system for creating an animated virtual object or digitalentity, the system comprising;

-   -   a plurality of module templates able to have a computational        element and a graphical element,    -   a first describing means which specifies the function and        variables of one or more selected modules, each of the selected        modules being based on one of the plurality of module templates,    -   a second describing means which specifies a plurality of        connections between the variables of the one or more selected        modules,    -   wherein the one or more selected modules are connected to as to        create a behavioural or neurobehavioural model.

In an embodiment at least one of the module templates is a neuron model.

In an embodiment at least one of the module templates is a delay model.

In an embodiment at least one of the module templates is an associationmodel.

In an embodiment the system further comprising a third describing meanswhich specifies the relationships between modules.

In an embodiment the relationship is hierarchical.

In an embodiment the structure or hierarchy may be representative ornon-representative of a biological system or structure.

In an embodiment each module can time-step.

In a further aspect the invention may broadly provide control of acomputer generated display, effect or avatar using a network of modulesof defined functionality connected to communicate using a format fortime-varying data wherein the connections introduce timing and/or delayattributes to the time-varying data dependent on the arrangement ofmodules in a network so that the responses caused by the network can beadjusted by rearranging the modules or the connections.

In a further aspect the invention may be broadly described as a computersystem operable to control a digital entity in response to data definingstimulus for the digital entity, the system comprising a network offunctional modules of code, the network operable to receive datacharacterising the stimulus and operable to generate data defining aresponse for the digital entity, wherein the network comprises codedefining:

-   -   one or more variables for each functional module, the variables        configured for a time-based data format standardised for the        network and associated with at least one connector carrying        time-varying data between transmitting and receiving variables        of modules;    -   location-reference data defined for each module to allow a        position of the module to be defined relative to one or more        other modules;    -   time-adjustors operable to adjust the timing of time-varying        data transferred between transmitting and receiving variables,        wherein the time-varying data is dependent on the position of a        module of a transmitting variable to a module receiving of a        receiving variable,    -   one or more functional operations defined for each of a        plurality of functional modules and operable on time-varying        data carried in the time-varying signals received at variables        defined for the functional module,    -   whereby operations on time-varying data received at two        receiving variables receiving data transferred from two        different functional modules have an effect that is adjustable        by an adjusted relative position of the functional modules,    -   whereby the response of the avatar is adjustable.

The time-adjustors may comprise a set of transmission lines operable tointerconnect transmitting and receiving variables and to introduce atime delay dependent on the difference in location of the modules of thetransmitting and receiving variables.

The network may comprise transformers operable to allow two or morevariables to be combined whereby two transmitting variables can beconnected with a single receiving connector.

The functional modules may comprise a wrapper operable to parse data ina time-based format to a given format to allow code operating on datathat is not in a time-based format to be used in a functional module inthe network which is connected using the standardised time based format.

The functional operations may have parameters that are adjustable toallow adjustment of the response of the avatar.

The functional operations may have parameters that are adjustabledepending on network parameters that propagate through the network. Thepropagation may start at a defined location in the network and propagatefrom that location whereby the parameter may adjust thefunctional-operations depending on the location of given modules and theextent of propagation of the propagated network parameters. The networkparameters may be modulating values.

The network may be operable to receive data or inputs to determinelocation-reference data.

As used herein, data is used broadly to cover encoded information andmay include instances of data-type and event types and may includestreamed data.

The network may include time adjustment means independent of therelative positions of functional-modules to allow delays ortime-advancements of time-varying data to be defined to adjust theresponse for the avatar to stimulus.

The functional operations may be operable to define associations ofcharacteristics of time varying data from transmitting variables of twoor more functional modules. The response for the avatar to stimulus maybe adjustable by adjustment of the relative positions of functionalmodules in the network and/or to adjustments to the functionaloperations of one or more functional modules.

The response of the avatar to stimulus as controlled by the system canbe configured by the functionality of the modules, the connection ofmodules and the relative position of modules in the network.

The time adjustment means may comprise a delay introduced to the data ina time-based format.

The network may comprise code defining a set of connectors connectingthe transformers and modules, each connector comprising a time adjusteroperable to delay to the time-varying signals.

The network may comprise code defining a set of transformers operable tocombine time-varying data from two or more transmitting variables so asto allow connection to a single receiving variables.

The operation of the network may dependent on both the functionality ofthe modules and the relative positions of modules in the network.

In some embodiments the transformers do not introduce any time delay asseen by the functional modules.

In an embodiment modules are selected and/or positioned by an operator.

In an embodiment system may comprise a configuration interface operableto receive adjustments to location-reference data.

The configuration interface may be operable to allow selected connectionof modules to configure the network whereby the control of the systemand/or responses of the avatar may be configured. The configuration maybe operable to display a representation of the relative positions of thefunctional modules and the connection of the modules. The configurationmay be operable to display a representation of the network. Theconfiguration may be operable to display the avatar. The configurationmay be selected to allow the user to observe the network operatingand/or adjust the module position and/or selection of the modules.

In some embodiments data characterising stimulus is received from acamera. In some embodiments the system may be operable to control asingle avatar or multiple avatars individually or collectively. In otherembodiments the system may be provided within the code of anapplication, such as a game, and data characterising the stimulus may bereceived within the game.

One or more systems may be used to generate multiple charactersrepresented by avatars where the similar networks are configureddifferently to diversify the characteristic responses of the avatars.This may be used to provide a set of avatars or digital entities withdifferent characteristic responses. Different configurations may beachieved by changing parameter settings. For instance differentpersonalities may be characterised by sensitivities or response levels(e.g. by changing threshold variables) to neurotransmitters,neuromodulators or other signals in the model. Different configurationscould also be achieved by adapting the system topology or layout,creating different types of structure in the neurobehavioural model. Thetopology or layout may be changed by adjusting connections betweenmodules, the function of modules or the structure or relationshipsbetween the modules.

Embodiments of the present invention allow a range of different types ofcode to be included in functional modules which are interconnected viaconnectors that use a standardised time-based format so diversefunctional code can be included in the same network.

Another aspect of the present invention provides a facial graphicsrendering system, comprising:

-   -   a graphics rendering layer which receives muscle        actuation/position data defining degrees of actuation of a set        of facial animation muscles and which generates graphics image        data;    -   a muscle actuation/integration layer receiving nerve actuation        data defining a degrees of nerve activation for a given set of        animation nerves and generating muscle actuation data for a set        of activation muscles defined for the muscle actuation layer;    -   a nerve activation layer receiving expression data defining an        expression and generating nerve activation data defining a        combination of animation nerves to be activated and defining a        degree of activation for each nerve.

Each layer may contain data defining properties of the nerves, musclesand skin/fat/etc. The muscle layer/graphics rendering layer receivesstimulus data and generates feedback date.

In another aspect the invention may broadly provide a computer systemoperable to control a digital entity in response to an externalstimulus, the system comprising a network of functional modules of code,the network operable to receive data characterising the stimulus andoperable to generate data defining a response for the digital entity,wherein the network comprises:

-   -   one or more variables for each functional module,    -   a structure to allow a position of the module to be defined        relative to one or more other modules;    -   one or more connectors, the one or more variables being        associated with at least one connector carrying data between        variables of modules;    -   wherein the connectors are selectively adjustable to connect        different modules to thereby change or adjust the behaviour of        the digital entity in response to the external stimulus.

In another aspect the invention may broadly provide a computerprogrammed or operable to implement the system of any one of thepreceding embodiments.

In another aspect the invention may broadly provide one or more computerreadable media storing computer-usable instructions that, when used by acomputing device, causes the computing device to implement the system ofany one of the preceding embodiments.

In another aspect the invention may broadly provide a method ofcontrolling a digital entity in response to an external stimulus, themethod comprising:

-   -   receiving data characterising the stimulus;    -   processing the data in a plurality of interconnected modules        together representative of a neuro-behavioural model to provide        an output defining a response of the digital entity to the        external stimulus;    -   altering a connection between one or more modules, or altering a        variable in one or more modules in response to the output.

In a further aspect the invention may broadly provide a computing deviceoperable to perform the method of controlling a digital entity.

In a further aspect the invention may broadly provide one or morecomputer readable media storing computer-usable instructions that, whenused by a computing device, causes the computing device to implement themethod of controlling a digital entity.

Any of the above described embodiments may relate to any of the aboveaspects.

According to a further aspect the present invention provides a methodand system substantially as herein described with reference to theaccompanying drawings.

Further aspects of this invention which should be considered in all itsnovel aspects will become apparent from the following description givenby way of example of a possible embodiment thereof.

Any discussion of the prior art throughout the specification should inno way be considered as an admission that such prior art is widely knownor forms part of common general knowledge in the field.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1a : Shows an embodiment of the invention where a model of thebrain is displayed;

FIG. 1b : Shows a schematic of a computer system for implementing thesystem.

FIG. 2: Shows an embodiment of the invention where a life-likevisualisation is displayed;

FIG. 3: Shows an embodiment of the invention where a plurality ofmodules are linked;

FIG. 4: Shows an embodiment of the invention where a plurality ofmodules are arranged in a folder structure;

FIG. 5: Shows an embodiment of the invention where a plurality ofdifferent modules are linked;

FIG. 6: Shows an embodiment of an eye-blink module having links tographical and computational systems;

FIG. 7: Shows a schematic view of an embodiment of a module having acomputational, graphical and transformation portion.

FIG. 8: Shows a schematic view of an embodiment of the system comprisinga complexgraphical output.

FIG. 9: Shows a schematic view of an embodiment of the systemsurrounding the cortex.

FIG. 10: Shows a system for controlling an avatar in response to datadefining stimulus.

FIG. 11: Shows a system similar to FIG. 10 but with the addition to thenetwork of example emotional reaction modules

FIG. 12: Shows the system of FIG. 11 in which a parameter which definesan aspect of the functional operation of a module is adjusted.

FIGS. 13 and 14: Show a system similar to the system of FIG. 12 in whichthe network has an additional Voice Recognition Module for multimodalrecognition of face and voice.

FIG. 15: Shows a model of a neurobehavioural system showing howdifferent neural systems and computational elements can be combined.

DESCRIPTION OF ONE OR MORE EMBODIMENTS

The invention is described herein is implemented using a computer.Referring to FIG. 1b the computer has an input means 201 for inputtinginformation, a processor means 202 and an output means 203. Theprocessor means for processing the information may communicate with amemory means 204 for storage or retrieval of information. Inputs orstimuli may originate from real-world stimuli comprising for example aninput from one or more of a camera, electromagnetic transducer, audiotransducer, keyboard or other known systems. Other stimuli includegraphical user interfaces, hardware consoles, streamed data, and datafrom cloud computers, computer indexes, the world-wide web or a varietyof sensors. The output means sends signals to a display unit or anothermachine e.g. robot. The memory means may be a computer readable mediumsuitable for storing code, the code executable on a processor.Alternatively the model or part thereof may be a circuit. Embodiments ofthe invention include models with applications in the form of any one ormore of the following games, consoles, vending machines andadvertisements, mobile devices and cloud-computing devices.

In an embodiment of the invention biological behaviour is simulatedthrough biological models which provide graphical outputs. Graphicaloutputs may refer to any form of visual or presented output. Forinstance the brain processes which give rise to behaviour and sociallearning are used to animate lifelike models of the face which caninteract with a user. In another embodiment of the invention the modelmay be applied to an interactive animation. The animation mayincorporate multi-scale computational models of basic neural systemsinvolved in interactive behaviour and learning. Each computational unitor module may function as a self-contained black-box, capable ofimplementing a range of models at any scale (e.g. from a single neuronto a network). The modules are then linkable to create a network orstructure which forms the model.

A neurobehavioural model uses underlying neural pathways or circuits tocreate behaviour. The neural circuits created may range in complexityfrom relatively simple feedback loops or neural nets to complexrepresentations of biological systems. Therefore the virtual objects ordigital entities include both large models of humans or animals, such asa baby face as well as any other model represented, or capable of beingused, in a virtual or computer created or implemented environment. Insome cases the objects or entities may not be complete, they may belimited to a portion of an entity, for instance a body portion such as ahand or face; in particular where a full model is not required. Anavatar or other representation of a person or object is included in thedefinition of a digital entity or virtual object. In some embodimentsthe character or behaviour of the digital entity or virtual object maybe variable through the neurobehavioural model. The system animates thedigital entity or virtual object so as to allow realistic movement orchange of the entity or object.

The animation may synthesize or replicate behaviour and present thisbehaviour through advanced 3D computer graphics models. In a broad sensethe model may provide a behavioural system which can adapt to externalstimuli, where external stimuli refer to stimuli separate from themodel's internal stimuli. For instance an embodiment may interact with aperson through a screen interface or may be implemented as a robot. Thisfunctionality may be achieved through neural type systems or a mixtureof neural type systems and functional replacements for neural systems.An embodiment of the system may be referred to as self-animated becausethe animation is performed from external stimuli using learned methodswithout it being necessary to intervene with the animation.

In an embodiment of the invention the graphical/animation elements ofthe model with the computational elements of the model are linked in arequired structure, preferably a hierarchical structure. The structuresallow sections of code to be contained or grouped, meaning that thesections can be reproduced or moved as a group of components. Thestructure may include dependent structures including tree-like elements.In an alternative arrangement the hierarchical structure may beimplemented in another form to create a required structure. In anembodiment multiple hierarchies may be used. An important feature of therequired structure is that it provides a further link between themodules, the link focusing on the relationships or physical orpseudo-physical arrangements. In this way the required structureprovides a backbone or relational structure for each of the modules inthe model. In a preferred embodiment the required structure is arrangedhierarchically so as to easily display and make understood thestructure. This allows an improved description of the model and allows amodeller to more efficiently build a model as the modules containinggraphical and computational elements are related in a clear andbuildable manner.

An embodiment of the invention may include a model defined by a seriesof modules in a hierarchical structure. This may be similar to the way aphysical element may be deconstructed into its composite or componentparts. Each module may have zero, one or a plurality of dependentmodules. The plurality of modules may form a tree-like structure. Thisstructure is used for or related to the graphical structure but is alsoincludes the computational elements. The computational elements may bedefined in separate but similarly required/hierarchical structure.Elements may refer to sections, sub modules or portions of code or linksto code to carry out the function. Having separate elements of codeallows separation of the control of different functionalities in eachmodule. Preferably modules may contain both (or either one of)computational and graphical elements. In a preferred embodiment eachmodule is capable of containing each element and requires only that theelement be activated. In this way the structure of the model is clearlyobservable when viewing the hierarchy and the relationships betweenmodules, their computational elements and graphical elements are clear.Hence the model may provide an improved method of creating aneurobehavioural or psychobehavioural animation. In some embodimentsmore elements may be present to provide additional features or toseparate the module structure. A sensing element may be included in a, aplurality or every module so as to allow inputs from internal orexternal stimuli.

The graphical elements typically include geometry, shader and textureinformation or code. These features of the graphical elements can beconnected and modified by external modules. The shaders and texturescould be used in the general purpose GPU (GPGPU) sense for computation.A typical implementation of a graphical element might be for a virtualface. The face geometry, textures and shaders may be kept in a directorycalled ‘face’. The face directory may also contain computationalelements associated with the face. In this way the graphical elementsand computational elements are contained in a single module in therequired structure, but are also separate to allow management andupdating or linking. In particular different graphical elements may beoperated, for instance to show the operation of the neural net ormovement of the face. For instance a computational element may feed amuscle activation variable from a face nucleus module to the shader oranimation deformation module which may:

-   -   deform vertices of the face geometry    -   modify the mapping of the texture data being read in (e.g. to        change the appearance of the skin based on expression due to        blood flow)    -   modify the shading calculations based on connecting strain        information calculated externally to the shader.

The required structure is complemented by the connections between theplurality of modules. These connections or links help to control thecomplex dynamics and inter-relationships between the computationalsystems and animation requirements. Connections may link between theinputs and outputs (variables) of any module or element in the modelincluding both graphical and computational modules. This communicationand flexibility between the graphical and computational aspects of themodel allows a designer/programmer or user to create a very complexmodel efficiently. There is no requirement to replicate features oractions in separate, or weakly linked, graphical and computationalsections of the model. In an embodiment of the invention the inputs andoutputs may be preferentially connected to or routed through a highlevel module so that a branch of the hierarchy may become substantiallyself-contained. The majority of the connections may then be made to thehigh level module to avoid reaching into the complexity of the modulesinside. The connections, and other model features, provide means tomodulate signals in the system. Modulation of signals allows forbehaviour to be trainable and the training to be efficient because thetraining is independent from the detail of the model. For instance aneurotransmitter can be implemented as a connection to multiple models,and its value can be varied to adapt the model or the model behaviour.

Connections may be made between the graphical and computational elementsof the modules and these connections provide the means to create acomplex and human-like simulation based on complex biological models.Connections may provide an association between a first and secondvariable (where a variable is an input and/or output of a module). Thisimproves on the prior art systems which allows creation of neural modelsbut which have limited graphics or animation and which limit theinterface/s between these. By combining the graphical and computationalelements feedback loops and the relationships between the animation andthe underlying model can be controlled and/or described. This alsoallows the model to be updated more efficiently because the inherentrelationships may be visible, including real-time and during updating oroptimisation.

In an embodiment of the invention each module is connected with othermodules to form a network or required structure. In this embodimentvariables (shown as points on the modules where lines join the module)are connected by connections (which may be referred to as transmissionlines) which connect the variables and may introduce a delay thatdepends on, or represents, the distance in the network betweeninterconnected modules. In some embodiments the connections determinethe delay using or dependent on location reference data associated withconnected modules. In other embodiments the delay is introduced withinthe module or in a separate delay module. In some embodiments a timeadvancement is used in place of a delay or no delay may be present. Theconnections may carry time-based data, in the form of a time-signalbetween modules. The modules operate on the signals in conjunction withother signals to generate a response used to control an avatar ordigital entity displayed to a user, for instance on a screen or otherdigital presentation device. Both the relative timing of receivedtime-based signals and the operations will affect the output of themodules. Therefore the responses of the digital entity, avatar orvirtual object and/or the characteristics of the responses may beaffected by any one or more of the: choice of modules, choice of themodule's functional operations, arrangement of the modules within thenetwork and/or their relative positions and the selection of connectionsbetween the modules.

As used herein the term connector or transmission line may be any lineof communication suitable to connect two or more variables and mayinclude an object oriented interface. The timing and/or delay attributesmay be affected by the connections between modules. For instance in anembodiment where in each time step variables are moved from atransmitting variable to a receiving variable the presence of anintervening module between the transmitting and receiving variableswould delay the communication of the data. In other embodiments theconnections themselves may have a timing or delay component. Preferablya standardised network or communication format is used so that allmodules may communicate between variables. This may require a wrapper orinitialisation of the module which defines how code inside the moduleproduces the standardised network format. In an alternative embodimentthe position of modules and the visual distance or other locationreference data of the modules may affect the timing.

An example model of a brain and facial features is shown in FIG. 1. Themodel may include biological, computational and graphical elements.Preferably the computational and graphical elements may be broadly orsubstantially based on biological systems. In one embodiment abiological modelling architecture allows a series of low level modulesto be connected, or built into groups which are then connected to formhigh level components. This may follow or be derived from anevolutionary layering structure or evolutionary neural structure inwhich simple basic modules are linked and combined to result in complexoverall features. The basic modules may provide the core functionalityof the modules with high level modules providing additionalfunctionality connected into this more basic system. The biologicalmodelling architecture is then used to build an animation system basedon biology. An advantage of the system is that a complex animated systemmay be constructed by building a plurality of separate, low levelmodules and the connections between them provide human-like oranimal-like capabilities to the model.

FIG. 1a demonstrates an overview of the model showing a representationof the brain including some surrounding features. The model may includesub-models of neural systems and neuro-anatomy including scientificmodel based systems and neural networks systems. In particularbiological neural networks of known types may be used. This structureenables visualization of the internal processes generated bycomputational models giving rise to behaviour. The structure alsoprovides a describable or understandable form of interaction between auser and the system. The model or modules may be driven by theoreticalmodels, data driven empirical models, a combination of these orsimplified models. In some embodiments the interactive nature of themodel allows the behaviour of the avatar or animation to be varied inorder to construct desired behaviour or test behavioural patterns orbiological effects.

The creation of an avatar, digital entity or animation such as that ofFIG. 1a requires a modelling methodology for construction, visualisationand animation of neural systems. A novel model environment and methodfor neural models is disclosed and may be referred to as brain language(BL). BL allows users to create animations and real-time visualisationsfrom biologically based neural network models and allows model effectsto be viewed in an interactive context. For instance FIG. 1 shows animage of the brain and eyes 21 of a model, sections of this 22, andvariables, inputs or outputs 23, 24, 25. Such a visual environment isnot only suitable for creating a model, it is also ideal for modeldevelopment and visualisation of the model. The visualisation may use auser interface to allow adjustment of the network or allow configurationinputs to be received by a user. The model may take inputs and provideoutputs visually, audibly or graphically, using cameras, microphones orany other sensors as required. Different forms of input may requiredifferent input modules with appropriate wrappers to incorporate thedata into the model.

The BL modelling environment provides two-way communication between auser and the model. In embodiments the model may interface with the userthrough visual and/or aural communications. This means that the modelmay make sounds, change orientation or position and react to the userdoing the same and that preferably these actions should be realistic andhuman-like. In one example the model may cry if the user is not lookingin the direction of the model. Alternatively the model may monitor andreact to sounds or actions in its environment. In further embodimentsthe sounds or actions in the environment may affect the operation of themodel over time. The interactions between the animation, the environment(through sensors) and a neural model are possible because the modellingenvironment provides a rich structure of interconnection for complexsystems. This provides a means to test, improve and optimise the model.

FIG. 2 demonstrates an animation output which is a 3D representation ofthe face and upper body of an infant. The output representation maydisplay model results as well as affecting the model. For example thesystem can analyse video and audio inputs in real time to react tocaregivers or peer behaviour using behavioural models. Similarly themodel may be affected by the direction the animation is looking and anyexternal sounds or actions. The external face may be represented usingbiomechanical information or modelling. In animation this is typicallybased on muscle shapes. In alternative embodiments the output may be aspart of a robot, a cartoon figure or other means. These may not directlyresemble human or human-like features but may share human-like action orresponses. In embodiments based on animal or human-like features thebiological basis of the model may allow for or require, realisticmodelling restrictions creating a more realistic model. As well as theanimated output 31 a number of variables, inputs or outputs 32 may alsobe shown to improve the understanding of the model.

The system may be able to both describe and animate a digital entity.The description allows the digital entity to be viewed through thestructure and arrangement of the model parts. This enables a user toefficiently construct a model as the design and animation are closelycoupled together, instead of requiring separate neural model andanimation models to be created and then coupled retrospectively. Thedescription of the model may comprise the runtime data and/or adescription of what the system is and how the parts of the system areconnected. The animation of the digital entity is closely coupled tothis description but adds computational and graphical informationrelating to how the system is run and how each part of the systemoperates in a time-step. The tight coupling of the model is a featurecreated by the modules which contain, and directly link graphical,computational and/or transformative elements so that each module forms asegment of the total model. This allows component level modules to bebuilt into a cohesive and coherent whole, or combined to form astructure of connected modules.

In an embodiment a module may include a muscle level component for afacial animation. This may be driven by a neurobehavioural model and mayhave a graphical element relating to the muscle and a computationalelement relating to the operation of the muscle. The muscle componentmay receive an input from the model suggesting a preferred position oraction to take. A neural network pattern generator may receive theinputs or expected outputs from a collection of similar musclecomponents and combine these to form a coherent output effect for alarger muscle or muscle region. Because of the low level control of thegraphical output very complex facial expressions can be formed. This isbecause the model is not simply trying to combine a series of possibleexpressions, or match a mesh of data points across a face but instead tobuild facial expressions based on the anatomical or biological systemsof an animal or human. Other embodiments, described later, may providecoherent facial expression through a combination of outputs orexpressions and finite element elasticity.

The low level control over graphical and computational elements of themodel also provides the ability to study aspects of the model at a rangeof levels of detail. For instance if the action of a particular muscleis important the model can be limited to showing this, while maintainingthe computation or operation of the rest of the model. Similarly themodel can display both output graphical animation and outputs regardingcomputation of the model, including graphical outputs of thiscomputation. For instance a model of a human baby may be used to explorethe effects of dopamine on blink rate. The primary graphical output maybe the face or head of the baby and its facial movements. However a plotof the dopamine level in the baby may also be visualised so as to make acomparison similar to that shown in FIG. 2. In a second example a modelof a human baby can be used to interact with a user to model the effectsof dopamine on reward learning of a particular behaviour—for examplewhen the baby makes a certain expression, and the user respondspositively then the learning effects of dopamine modulated plasticitymeans this expression becomes more likely to be repeated. The user cansee the change in the baby's facial behaviour and also visualize thechange in the synaptic weights of a striatal neural network.

In an embodiment visualisation of simulated neural circuits can allow auser to see the neural circuits giving rise to behaviour in action inneuroanatomical context at any given time, or in more schematicdisplays. A feature of the model is to graphically look below the skin,to see the activity of the neural circuit models contributing to theactivation the facial muscles. The range of viewing modalities availablein BL allows users to viewer various parts of a model in aneuroanatomical context at will as well as offering more traditional“numerically focused” displays which may be better suited for moreabstract models and for live model parameter modification.

The visualisation could be achieved by adding a graphical element, orvisualisation element to a dopamine variable or connection from adopamine variable in an appropriate module in the model. The user maythen want to examine the effect of a drug on the dopamine/reward system.This may involve adding a connector from the drug to a module of theneurobehavioural model. The user may want to see how this effects theoperation of some portion of the neural system. Again this could beachieved by creating or activating a graphical or visualisation elementassociated with that portion of the system, and this may be activated ata plurality of levels, from a component of the face to an individualneuron module. This is possible because the simulation is built from acombination of modules with a required structure, the modules havingcomputational and graphical elements so that both the computation ordata based processing and the graphical processing can be investigated,described and adapted, either individually or separably. The modulebased approach also allows further detail, in either computation ordisplay elements, to be added by introducing further modules in therequired structure. In this way the state of the model which generates,for instance, facial behaviour can be visualized through graphs andschematics or by exploring the activity mapped to the underlyingneuroanatomy.

Animation/Graphics

FIG. 2 shows an animation having a human-like face. The generation andanimation of a realistic face and facial expressions may be achievedthrough the use of the model. A neural control system is preferred as agenerative model for facial animation as it constructs facial motionfrom the building blocks of expression. This may help to create a moreconsistant overall expression in the digital entity or virtual object.The neural control of facial movements requires the use of multipleparallel systems including voluntary and emotive systems which areanatomically and functionally distinct up to the facial nucleus. Theability to have control of the facial animation or expression based onconnections to the neural system provides means to produce realisticanimation and configure and optimize the animation so as to make it morehuman-like. The facial animation of the model may use a neuroanatomicalmodel based on the architecture of the facial motor system. This maytake inputs from other modules associated with the model, althoughpreferably will be based on the known scientific models. In anembodiment of the system the face, or other graphical feature, may forma separate portion of the structure or a separate structure in order tofocus on the graphical requirements of a realistic face. This facialstructure would then be connected or linked to the neurobehaviouralmodel in order to be controlled.

In an embodiment of the invention a complex graphical output, such as aface, may be formed by a series of modules that contain only, or largelygraphical data. In this way the face may be more independent of thecomputational aspect to allow changing of facial (or other graphicalimage) details. The face may be arranged as a set of modules, with afirst module representing the face and a plurality of modules in arequired structure then representing sub features. In a first examplethe modules may represent only the surface of the face, or in a secondexample the modules may represent the face and muscle or tissue elementsbehind the face. The features of a face may be obtained as discussedabove where a series of facial expressions is calculated recorded anddescribed. In an embodiment the facial expressions may be blended in themodel to create a composite expression. An advantage of using blendedfacial expressions is that this ensures that an expression is completeand uses the entirety of the required muscles. For instance a person'sforced smile may be differentiable from a real smile by thenon-symmetrical nature of the smile as individual muscles are beinginstructed instead of an organised smile pattern of muscles operatingtogether.

Referring now to FIG. 8 an embodiment of the invention shows the facialexpressions of a face controlled through a plurality of modules. Theface is represented by a plurality of muscle shapes 81 or facialfeatures which represent the surface of the dace. The actions andgraphical representation of these muscle shapes is provide by a seriesof expression modules 84, 85, 86. The expression modules may havepredefined weightings for the muscle shapes 81 so that when triggeredthey provide the weightings, strengths or inputs to the face to createthe appropriate expression. The predefined weightings may be obtainedfrom earlier data capture. For instance the expression modules mayrelate to a frown 84, smile 85 and angry expression 86. The facialexpressions may be controlled by one or more pattern generators used asexpression generators 87, 88, 89. The expression generators 87-89provide a means to blend and shape multiple expression modules. Forinstance a parent may require an angry frown which could be achieved byblending the angry expression and frown faces. In other examples theexpressions may be more distinct, for instance combining smile andsurprise to create an overall expression of a pleasant surprise. Theexpression generators may include pre-trained response curves based ontheir input variables.

The expression generators may be linked to the neurobehavioural model,and in particular to modules directed to emotion. For example they maybe connected to modules for punishment 90 or reward 91. The expressiongenerators may take an input of the any one or more of the apparentemotions of the system and configure a suitable blend of facialexpressions for the output. The system may be contained under an overallstructural element 95 in order to provide the required structure andsupport the organisation of the system. In a preferred embodiment thesystem includes one or more learning elements associated with the facialexpression system. For instance the learning elements may change theweights of the expression generators so that a model which has beenrewarded for an extended period of time for making a particularexpression has a stronger weighting towards the rewarded expression soexhibits the expression more frequently. Learning through conditioningcan also be made by the model. For example an associative module whichassociates stimulus induced sensory activity with emotional triggeringof behaviour after conditioning will trigger the behaviour throughexposure to the stimulus alone. This is analogous to ‘classicalconditioning’ or Pavlovian conditioning.

The graphical output or animation may be composed through the use ofbrainstem or cortical pattern generators, modelled using biologicallyplausible recurrent neural network models formed as modules, and produceactivity which is resolved in the facial nucleus before being output asanimation weights. The graphical output of the model, and the particularface model, as shown is designed to be driven by muscle activationsgenerated from simulated motor neurons. The motor neurons may becontained in a separate module or may form part of the graphical portionof a module. The facial expression geometric manifold is created bymodelling the effect of individual muscle activations and significantlynon-linear combined activations. The graphical animation may useprocedures including scanning, geometric modelling and biomechanicalsimulation.

The facial expression manifold may be generated using a comprehensivebiomechanical simulation of individual muscle activations. This providesan advantage that the graphical output is a combination of each of anarray of individual features but forms a single coherent and flexiblewhole. The system may also allow sections to be turned on or off so thatobscured portions may be viewed, or certain features may be concentratedon. When the model contained in the system is simulating a human brain,or parts thereof, switching off certain sections of the computationalmodel may cause the system to behave as though it had a received a braininjury. This may be referred to as a synthetic lesion.

The system may also allow facial expressions to be artistically modelledso as to allow the construction of stylized characters or cartoonssharing human-like qualities. An advantage of using physically basedmodels is the compatibility with future robotic implementations of themodel, for instance in a humanoid type robot. The facial expression maybe based on a limited set of face biomechanics however in a preferredembodiment the structure is closely matched to the anatomy of the face.Digital entities may include avatars as well as displays or audio-visualoutputs including lighting, cartoon characters, colours or various otheroutputs or displays known to the reader.

The system may include models of deep and superficial fat layers, deepand superficial muscle, fascia and/or connective tissue. The effects ofdeformation or facial movement on the face may be acquired through theuse of large deformation Finite Element Elasticity, which is used todeform the face from rest position through simulated muscle activation.The action of each muscle in isolation and in combination with commonlyco-activated muscles may be simulated to create a piecewise linearexpression manifold, or a pattern of facial movements. In an embodimentthis pre-computed facial geometry is combined in the model to createreal time facial animation with realistic deformation and skin motion.However other methods may be developed to create pre-computed geometriesor process geometries on the fly. In an embodiment of the invention thegraphical output may be used to drive a robot face or similar physicalrepresentation. Alongside the physical representation of a face thegraphical output may be able to display further details about thegraphical output such as the tension or stretch or stress.

Learning Elements

The model also allows for learning and/or episodic memory elements.These elements may be implemented in the form of modules which store aset of data values or weights. The weights may be related to a neuralnetwork. In a an embodiment at least some of the weights aresynapse-like weights. The weights may change or adapt to a change in theenvironment or inputs of a model. For instance they could react todopamine levels, where a higher level may indicate a more plastic state.In this way the neural network of the model may adjust automatically andthe outcomes may alter with time. The dynamics of social learning arekey in developing realistic interaction, as a party or user interactingwith the baby would expect the baby to change its response on provisionof positive or negative reinforcing feedback. The learning modules mayform a part of a computation portion of a module and may also be shownin graphical form as described above if required.

The learning modules may be associate learning elements or associationengines or modules. For example the model may be trained so that itlearns by reacting to earlier events. A positive reinforcement cycle mayoccur when a reaction with a user appears to show a successful outcome,the successful outcome encourages the reinforcement of the weights orpathways through the system. An association between two variables,modules, or inputs and outputs indicates a relationship forming betweenthem wherein a change in a first causes, directly or indirectly thelatter one to change. In some cases multiple associations may be formedto or from the same module/variable or stimulus. This means that thenext time the same event occurs it will be preferential in a similarway. A second example may use a negative example where the weights arechanged based on a negative response to some stimuli. Over a period ofinteractions this allows a preferential set of weights to be built in anassociation engine and for the reactions of the model to change andadapt. In some embodiments the association between a stimuli and weightsmay be indirect, filtered or effected by a path through the system orother learning modules. In some case, particularly when focussed onbiological systems, the learning process may be suitably indirect thatthe full effect of the change is observable only in the behaviour of thevirtual object. I.e. it is not easy to ascertain how particular weightshave changed to cause an effect.

In some embodiments the learning modules may be dependent on theinternal activity, or internal sources or stimulus. This allows thesystem to create an internal world rather than be dependent solely onexternal stimuli. For example, internal homeostatic imbalance e.g.tiredness could affect behaviour, without being related to or associatedwith an external input. Or behaviour may be affected by history/memorywhich are internally generated, so would be an indirect internalresponse. In further embodiments the neural networks, analogous to aweather system, may be constantly evolving in their activity, as some ofthem exhibit chaotic behaviour, depending in a nonlinear way on theirpast state. In one embodiment the system includes neural networksdesigned as central pattern generators which generate certainbehavioural actions even in isolation from external stimuli. For examplebabbling may be created by a central pattern generator (CPG) operatingrhythmically and this may provide a basis for a learning module. Inother embodiments the learning networks may form associations betweeninternal stimuli, the feedback loop having only an indirect externaloutput or being primarily focussed on changes between the interactionsbetween modules.

The CPG's are not known to have been used in digital animation andprovide a way of creating time series of activity (potentially cyclic orrhythmic). This activity can be a core feature of the system, beingbiologically plausible and allowing the neural networks or computationalelements of modules to generate patterns. For example behaviours likecrying or laughing or breathing are driven by central patterngenerators. The central pattern generators may be constructed or codedby fitting animations to Recurrent Neural Networks (RNNs) using afitting algorithm. On activation of the RNN (by for instance anactivation signal) a pattern may be generated. Th generated patternsintroduce variability into the animation, so the animation doesn'texactly repeat (as it is created by, or has as a part, a potentiallychaotic system), but has a similar repetition. For instance this mayprovide variability in response based on similar inputs, which weassociate with some biological behaviour.

In some embodiments it may be desirable to fix the associations of amodule. For instance a model may be trained so as to act in a particularway and this may not need to be changed. Alternatively the ability ofthe system to change or adapt may change with time or neurobehaviouralelements. In this case the association engine or module may be referredto as fixed or a pattern module. A pattern module may be used forgraphical elements including pre-built appearances including facialexpressions. There may be a plasticity variable which can control theamount of change possible to an association module or learning module.This may function by limiting the possible change in weights for anytime step. The plasticity of a module allows the speed of change to beadjusted and may be optimized to trade-off between retention of memoryand pick-up of new memory. In some cases weights in a single module mayhave varying plasticity. The plasticity may be influenced by feedback aswell as external modulatory signals such as from neurotransmitters.

For example the association module may be used to teach a module to playa simple game such as pong. The model may have stimulus which is thevisual input of a pong screen, and output the position of the paddle bymoving its eyes. A reinforcement means may be provided through anincrease in a neurotransmitter such as dopamine. When the modelsuccessfully hits the ball with a paddle through motor babbling, themodel receives more dopamine and the neurobehavioural system mayassociate this with the tracking of the ball/paddle and reinforce theassociation modules relating to this.

Example Models

Referring now to FIG. 6 the flexibility of the model is shown through animplementation of an eye blink. This feature could be incorporated aspart of the face structure of the face 61, eyes 62 and cornea 63described and shown in FIG. 5. The blinking of the eye is coordinated bythe oculomotor system so that blinks tend to occur during saccades. Inone embodiment the system may be modelled by a module with anappropriate neural network. In an animated model the eye-blink systemmust also have a graphical element—linked to the eyelid and the musclesaround the eye. These connections are made straightforward because ofthe structured and combined nature of the eye system and thecomputational data being added. However the eye also has complicatedrelationships with other areas of the brain. For example eye blinkingrate may be affected by dopaminergic activity. The structure of themodel allows for this completely different and complicated system toprovide an input into the eye-blink system, without alteration to theeye system. If there was some form of feedback to the nervous systemthis could also be simply connected. The ability to interconnect the twosystems may rely on the hierarchical structure of the model and theseparate nature of each module.

In a further example we may consider what happens when a change inenvironment occurs, for instance when a change occurs in the visualfield of a model this is detected by a camera. The camera output ismapped to the simulated retina which detects luminance change and mapsto activity on the superior colliculus which resolves competing inputsand directs the oculomotor system (comprised of multiple nuclei) togenerate saccadic eye motion which is sent to the animation system.Unexpected stimuli cause dopamine release via the tectonigral pathway.The eyes foveate on the stimuli if novel or rewarding dopamine isreleased which affects the Basal Ganglia, modifying current motoractivity and future response through hebbian plasticity. The amygdalaassociates the current emotional state with the stimuli, and may triggerhormone release in the hypothalamus and activation of brainstem motorcircuits driving facial muscles which produce animation by activatingpre-computed biomechanically simulated deformations. The response andplasticity of the subsystems are affected by the levels of differentneuromodulators and hormones, which also influence the affective state.Because the behaviour of the model is affected by its history as well asexternal events animation results from complex nonlinear systemdynamics, self-regulated through parametric physiological constraints.This describes a particular set of reactions based on a visualenvironmental change and biological understanding of the neural systems.However it should be understood that the complexity may not onlymanifest through the model described and the model may be varied toincorporate or remove sections or features as desired.

Applications

To provide examples of possible uses of the described system and methodapplications involving a game environment and an advertising environmentare discussed briefly. However, the invention may not only be limited tothese embodiments. In a gaming environment a user may control ananimated character or virtual object which interacts with other in-gamecharacters. In many games these characters are very simply orzombie-like in their actions.

These in-game characters may be implemented using an embodiment of theinvention, helping to overcome the computer-like or robot-like nature orcharacter of many in-game characters. The computer-like nature caninclude having constant interactions or having interactions that do notchange.

Characters based on neurobehavioural models can have abroad range ofskills or lack of skills while representing very realistic interactions.Furthermore a game developer can adjust the characters, or allowcharacters to learn, in a straightforward manner so as to provide a widerange of different character types depending on application. Forinstance characters could be modelled to be more or less receptive to aneurotransmitter or other variable (neuro-modulation), such as oxytocinor dopamine, or have slower reaction times depending on the desiredusage of that character. The interconnected graphics portion of themodel make these changes clear and convincing, for instance a characterwho is upset may appear upset because of the interaction, and this upsetfacial expression may be realistic rather and/or smoothly changing basedon the interaction between the user and the character. This may providefurther advantages because it enables the creation of a plurality ofcharacters with different interaction abilities to create a complexworld.

A character with learning aspects or modules allows an interactionbetween the character and user to be remembered and the neurobehaviouralmodel will be realistic when the characters meet again. For instance acharacter to which a user is kind may have a learning module thatremembers this and interacts positively at the next interaction. In thisway interactions between a user and a character will be both realisticgraphically and will develop over a series of interactions in arealistic or interesting way.

In a further example the system or method may be applied to orassociated with an advertising means, display screen or webpage. In thisexample the model may be interacting with a user to present an idea orproduct. The model may have a stimulus which is visual input of theusers face and provide an animated visual output which the user can see.The user's interaction is not one-sided but interactive, where adifferent response of the user to some element of information may causethe model to process the response and make a choice about what topresent next or how to present this information. At the same time thegraphical or visual element of the model presents a realistic andemotive facial expression so as to improve connection with a user.Because the model combines the graphical and processing elements it canbuild a strong connection which, at least in part, may bridge theuncanny valley effects. The neurobehavioural information of the modelmay also be used to judge the effectiveness of the advertising. Againthe processing or graphics of the model can be adapted so as to providethe desired service. For instance a drive-through restaurant may have amodel taking orders which has human-like qualities so as to overcome anyanti-computer bias but may also be represented by a graphical outputsimilar to a character associated with the restaurant.

In a further example the system may be used in a human interactionenvironment. Airport check-in is often personnel intensive and requiresa high through-put of passengers. However, limited success has been hadusing avatars or digital entities to interface with passengers. This ispartially due to the lack of interactivity of avatars. However aneurobehavioural based avatar may be capable of changing behaviour basedon passenger response to provide a better service. This may be achievedby monitoring the passenger visually or by reviewing answers provided bythe passenger. The model described provides a system capable of sensingand changing behaviour as well as being expandable or adaptable fordifferent situations or passenger interaction points.

System Operation

The system may run on a general purpose computer. The architecture ofthe system may comprise a plurality of levels or data structures. In oneembodiment there are a plurality of levels in the model architecture.The levels may comprise

-   -   Programming defining each module type and function;    -   Structure combining and ordering a plurality of modules; and    -   Structure providing linkages and communication between modules

The separation of the model into a hierarchy comprising modules atdifferent levels of detail allows broad interconnecting between modulesin the model because there is clear separation between modules but theoverall structure provides a means of connecting them. For instance if aconnection is to be made to the eye, the eye may be separate from theface or nose allowing new connections to be made to the eye withoutaffecting the remaining model. The organisational structure of the modelalso allows the eye to be easily found and the link created. In someembodiments connections may be made between substantially any of thevariables in the model. This may allow graphics to be interfaced withneural models and the formation of complex animated systems. In someembodiments the described structure provides an ability to createcomplex animations in a straightforward manner because it separates anddistinguishes design levels and skills.

This allows a first user to create the modules; a second user tostructure and group the models appropriately and a third user to makeconnections and data flows between modules. The connections may be seenas a way of describing the interrelation between modules, or thevariables of module or the system. In some instances a single user mayperform each of the tasks, but may do so in a separated manner so as onesection may be updated without affecting the system or requiring largerestructuring or reorganisation of the system. In this way a model maybe constructed from a library or collection of modules or moduletemplates. This allows the separation of the modelling into acoding/preparation portion and an assembly (of modules/module templates)and linking (of connectors) portion. The assembly portion does notnecessarily require the understanding or skillset of the programmingportion and can be performed in a straightforward manner by one skilledin the structure of the model. The module templates may be broaddescription of modules without clearly defined functionality. These maybe refined by the addition of functionality or graphics, for instancefrom known scientific models to form modules. The library may comprise amixture of modules and module templates depending on the proposed methodof construction. The library may include a set of modules capable ofcreating a behavioural structure or neurobehavioural structure.

In an embodiment the operator or user selects modules to include in themodel or network, selects connections between modules and adjustsparameters associated with the modules and the relative position ofmodules in the required structure. The operator then observes thedigital entity or avatar displayed at the screen and adjustscombinations of the module selection, position, parameters andconnection of the modules which affects the characteristics of theresponse of the avatar controlled by the model. For instance thecharacter of the avatar could be affected or configured by changing thetopology or layout of the required structure. New modules could beadded, module sections replicated or structure portions (e.g. treebranches) removed. The changes in connections between modules or systemwould also affect character—for instance if a neurotransmitter was nolonger transmitted between modules or the transmission was lesseffective. The amount of configuration required would depend on thelevel of differentiation, or the importance of the differentiationbetween the behaviour of avatars or virtual objects.

In an embodiment the extra information processing capability is based onthe precise timing of different signals is an adjustable parameter tothe system. Thus the outcome of the model may be dependent on howinformation is connected through the model. In an embodiment therelative position of the modules may be a parameter. This enablesmovement of the modules to cause location-reference data to be adjusted.In an embodiment a purely temporal reference frame is used in place ofpositional reference frame.

The modules should first, or before use, be adapted to be recognised bythe model. The structure or hierarchy of the plurality of modules canthen be created. Each point or node on the structure may be linked toother nodes and may contain a link or pointer to a module from thelibrary. The nodes may occur with and be referred to as modules. Themodule computational element or code may be contained directly in amodule at the node. After the required structure or dependency tree isbuilt connections can be made between the nodes on the structure. Theseconnections may be viewed as a web over the branches of a tree. Theconnections enable the basic dependency to be transformed into a complexand developing system by linking the points (using modules andconnections) in substantially non-limited ways. The connections may bemade without requiring the structure to be recompiled. In this way themodel may be updated, tested or optimised in real-time and with fastresponses. The dependency tree may be referred to as a means ofdescribing the system and the relationships between modules may becreated through a programming language.

A feature of the model is that modules may represent the system atdifferent levels of detail. For instance, based on the hierarchicalstructure and design of the model a simple basic system may beimplemented to provide the basic functions of a model. The portion orbranch of the structure associated with a feature of interest can behighly developed, to a high level of detail (low level system) with manyassociated neurons or computation complexity, while the remaining modeloperates at a relatively low level of detail (high level system). Thelevel of detail required or used may be adapted dependent on thesituation or processing power available. This may be particularlyrelevant for models where it is difficult to anticipate the amount ofdetail required. Where previous models required the level of detail tobe adjusted or chosen before building the model the flexibility of thissystem may allow for continuous manipulation of the level of detail.

Referring now to FIG. 5 a schematic of a portion of an example model 1is shown. The schematic demonstrates the links, structure andinteraction between the graphical, computational and biological natureof the model. Beginning at the bottom of the figure a vision module 50is shown. The vision module may be implemented using a camera or otheroptical sensor, or a stand-in device. The module produces outputsincluding a pixel array 53, with a width 51 and height 52 and acomposition number 54 (to distinguish between RGB and grayscale). Theseoutputs may be connected to other parts of the module. For instance,they may become inputs to a face detection module 55. The face detectionmodule 55 may be programmed, or otherwise adapted, to detect facestructures from an image, producing outputs describing the face 56, theface's coordinate position 57, 58, size 59 and a mask of the image 60.Similarly the outputs from the vision module 50 could be connected to amotion detector 56 as shown, or a range of other modules as needed.

A feature of the described system is that a module, such as the visionmodule 50, may have any given structure inside it. For example, thecamera module may first be implemented as a black box for testingpurposes, providing a known or constant input. When required anappropriate imaging system or camera could be inserted into the module.This process requires that a wrapper or identifying structure isorganised, coded or written around the input device (or model) so as toform a module. The wrapper or module definer tells the model how tointeract with the module. Applying the wrapper may require specifyingthe inputs and outputs of the module and a time step action. After themodule is prepared introducing the module into the model requireslinking the module to a section of the model hierarchy and linking theinputs and/or outputs (as defined by the wrapper) to other modules. Thewrapper or model definition introduces or defines new modules containingdifferent systems or model types into the system or model in a way thatthe models remain distinct but are relatable and connectable. Thefeatures of a module are not limited by the model it is placed in. Themodel has the ability to incorporate a plurality of different moduleswith different kinetics or operations or neurons and combine them in asimple way. The available modules may form a library or selection listin which a plurality of premade modules may be incorporated into themodel multiple times.

Module types may refer to specific module instances but is preferablyreferring to the central or overall function of the module. For examplea module type may be graphical where it has a primary or sole functionof displaying an input variable or of displaying a graphic using theinstructions of the input variable. Similarly an animation moduledisplays a graphical element but also allows movement of the graphicalelement. A module type may be computational where is has a primary orsole function of processing the inputs into outputs. There may bedifferent types or sub-types of modules such as computational models.For instance a module may provide a relatively simple computation suchas a ‘winner takes all module’ of FIG. 5, or more complicated modelssuch as neurobehavioural models or bio-mimicry models. These may bereferred to a simple computation modules and scientific computationmodules respectively or other terms may be used to differentiate betweensub-types of modules. For example a neuron type module will comprise oneor more neurons between the inputs and outputs. An interface module mayreceive inputs from external sources or provide outputs to externalsources, for instance a camera module would be designed as an inputinterface. A module may be a container or structure module with limitedor no function but creating structure or organisation for other modules.A learning module may have a memory or storage elements to providechanging behaviour. A module type may also refer to the level of detailof the module, for instance high- or low-level modules.

Vision module 50, face detection 55 and motion detector 56 may beorganised hierarchically below a visual systems module 57. The visualsystem module 57 may be a container or may also comprise or containoperational code or instructions. The visual system may also link toprocessing modules. FIG. 5 demonstrates a neural network module, actingas a salience map 58. The salience map module 58 may take inputs fromthe face and motion detectors and produce a series of outputs relatingto out the important features of the vision image. This may be achievedby any of the commonly known means. In some embodiments a biologicaltype neural network may be used or a model based on biological systems.The output of the salience map 58 may then be processed, for instance bya ‘winner takes all’ module which isolates the strongest feature. As isshown in FIG. 5 the structure of the model has allowed all the buildingblocks of the visual system to be contained or held together, positionedor related in an organised manner. However, the system also allowsconnections between modules whether those modules are in the samestructure or in an unrelated structure or a different container module.

The visual system module 57 is shown in FIG. 5 contained in the headmodule 58. The head module also contains a gaze system module 59, abrain module 60 and a face module 61. The selection of modules possibleis not limited to those shown. The modules may each comprise someportion of the systems required to build a model of the head. Forinstance the gaze system module 59 is in the head module and providesinstructions for which way the eyes are looking. This may require, atleast, inputs from the visual system 57, the face system 61 and outputsto the face 61. However further connections may be made to the brain ormodule 60 or another module such as the eyes 62 or pupils (not shown).The connections between modules, either at the container level ordirectly to sub-modules provide great flexibility in the operation ofthe model. In a sense, any feature is represented as one or more moduleswhose inputs and/or outputs may be connected to any other input oroutput by a connector. The use of a modular system allows a layout ororganisation of the components of the model, providing both a visibleform of the model and allowing configuration and/or reconfiguration ofthe model and its neural circuits.

In an embodiment of the invention the graphical elements are integral tothe model. This overcomes problems associated with maintaining a complexbiological model for a brain and the animation required for realisticmovement separately. In the embodiment shown in FIG. 5 the face module61 contains the graphics and animation for a modelled face. The facemodule and child modules such as eyes 62 and cornea 63 are contained inthe structure and hierarchy of the module alongside the processing andcalculating modules discussed previously. In further embodiments theremay be further relationships between modules. For instance a connectionmay be formed between the eyes module 62 and the vision module 50 so asthat the camera looks in the direction of the eyes. The combination ofgraphic elements with the computational elements is complex as eachsystem is inherently complex involving many interconnections. Howeverthe use of the same or closely corresponding structures for each systemprovides simplicity and an understandable system. This is, in part, dueto the avoidance of redundancy between the different systems and theclarity of the resulting model to a user.

The hierarchical relationship of and between the graphical elements, thesensor elements and the processing/computational elements provides astructure in which a complex system can be built and understood. Insteadof requiring a complex structure to be carefully planned and optimisedthe model may be built up and improved in a piecewise manner. Forinstance a modeller/user could have begun by defining the structure ofthe face and its components. After building the face 61, eyes 62 andcornea 63 the appropriate graphics could be included in the modulehierarchy. Then, perhaps based on a biological model, a simple feedbackloop could be programmed from a visual module 50 to direct the eyes.Because of the hierarchical structure described the movement of the eyesis dependent on the movement of the head, leading to a consistent andrealistic movement. The effect may be passed through the hierarchy usinga transformation instruction contained in each module. The eye movementmay also be linked to a visual input which identifies interestingenvironmental features. This input may, through the link or connection,provide a directional input to the eye. The eye system may take thecombination of these inputs and act so as to match the graphical andcomputational inputs. New features can be subsumed into the existingmodel by additional modules and connections being added. This allows abackbone of modules to be created and then additional complexity ofgraphical models to be added as appropriate and without having torebuild the model from the beginning.

In an embodiment the model may be understood by considering informationflow from the inputs (for example audial/visual external inputs) to theoutputs. The required structure provides a first flow of informationthrough the model. In particular the required structure provides alayout or configuration of the modules so that information is shared tomodules that are dependent on other modules. This configuration providesinformation flow that is suited to understand systems or physicalstructure where dependencies are clear and replication or repetition ofportions of the structure is desirable. A second information flow isprovided by the connections between module variables. This informationflow is built, preferably in a web-like structure over the requiredstructure so as to define links between modules that are not dependenton the required structure.

For example these connections are particularly suited toneurotransmitters/neuromodulators or similar which have wide rangingeffects on a plurality of modules across the model. The connection orlinks also allow for modification of the connections or new connectionsto be tested. As these are at least partially independent from therequired structure considerable variation is possible to the connectionswhile the required structure ensures the model remains consistent as awhole. The relative complexity of the required structure and theconnections can be varied. For instance in a complicated model it may bedesirable to use the required structure as a substantial framework withconnections making links between it. However, for greater flexibility asimple required structure could be used with a greater number ofconnections then made to transfer information around the structure.

The required structure may demonstrate a clear relationship ordependency between two modules. For example the required structure mayrepresent the physical relationship of a series of modules (e.g. head,face, eyes, cornea and pupil). While in a model having a limitedrequired structure this information may be transferred by connectionsthis makes it more difficult and less efficient to represent theportions of the model visually and for a user to modify theconfiguration of the module. The connections must transfer more databetween the modules and the connections must be carefully constructed toensure that important or necessary relationships are not removed. Theconnections then link variables between modules without establishing adependency between the modules themselves.

In some embodiments the required structure may form an important part ofthe systems operation. Preferably the operation is time-stepped. Thetime-stepping should occur at a rate fast enough that the animation oroutput appears fluid. A frame-rate of 40 frames per second may beperceived as fluid and requires a new frame every 25 ms. Given acomputation time of 1.5-2.5 ms this allows approximately 10computational time-steps before a redraw is required—although thegraphical output may be redrawn more or less frequently if desired andthe computational time may change dependent on system parameters andmodel complexity. In an embodiment the main operation may involve eachmodule taking a time step. In this time step the module computationelements may be updated. Typically the graphical element does not haveany change in a time-step. Each connector then updates with the newvariables and transports these to these variables to the connectedmodules. Then, preferably by working from the top of the requiredstructure to the bottom, following the transformation instructions themodel can be redrawn. As described above the time step and connectorupdates can be repeated multiple times before a redraw. Similarly somemodules may have breaks or holds so that they do not update asfrequently as the rest of the model.

The model may be implemented by having a first library or collection, orlist which includes vary broad template modules or module types such asneuron modules, association modules etc. This library may be used tobuild a second library or set of modules which implement or describe aparticular method or model inside the module template. A description ofthe links between each of, or each instantiation of (as modules arepreferably usable multiple times in one model), the modules is thenwritten, preferably in BL. This description explains how the modules areconnected and which modules have graphical outputs. A furtherdescription or code may be used to provide further structure or linksbetween modules. In a preferred embodiment at least one of thedescriptions are based on a file-structure, where the modules may bearranged in the file structure to establish an arrangement, requiredstructure or hierarchy. Such a system provides a high degree offlexibility in changing the model and separates the complexity ofcreating single models and the complexity of combining a range ofmodules appropriately.

Neurobehavioural Model of the Cortex

Referring now to FIG. 9 a schematic is shown of the cortex 20 and aselection of connections to and between related modules. This may bereferred to as the cortico-thalami-basal ganglia-loop. The cortex modulemay have neurons module/s 23 which integrate activity of incomingmodules and/or synapse weights modules 24 or association modules whichcan do plasticity or change effects over time. An input to the cortex 20comes from a sensory map 21. The sensory map may be used to process thedata received from an external stimulus such as a camera 17. The sensorymap 21 functions as a translation form the pixels of the stimulus toneurons which may be inputted into the cortex.

The cortex may have feedback connections 33 with other modules such asthe thalamus 22. The feedback loops can be used to provide a means ofintegrating sensory perception into the cortex. A positive feedback loopmay help associate a visual event or stimuli with an action. The cortexis also connected to the basal ganglia. The basal ganglia 29 may have aplurality of related or sub-modules which include neuron modules andsynapse weights modules and may provide feedback to the cortex or to thecortex via the thalamus. Although only single connections 31, 32 areshown to the basal ganglia multiple connections may be made and furtherconnections may link to shown or non-shown modules. The basal ganglia 29itself, or modules connected to the path may modulate the feedbackbetween the cortex and thalamus. That is the intermediate module withneural functionality may increase the complexity or adaptability of thestructure. A neurotransmitter/neuromodulator 25 such as dopamine oroxytocin may be used to effect the operation of the structure. This maybe implemented as a connection from another portion of the module, orexternal stimuli. In a preferred embodiment a neurotransmitter such asdopamine would link from a reward value 26, where a high reward valuewould be positive and release more dopamine.

The cortex may also be linked to an output means, in the shown diagramthis is a motor output means to muscle activation 28 which is connectedthrough a brainstem module 27. The brainstem may contain patterngenerators or recurrent neural network modules which have pre-set orblend-able pre-set muscle activations 28. Output means may also be usedto display the operation of any of the modules in the model. These areseparate from the display of the animation or muscle activation andallow changes in variables, synapse weights or other features of themodel to be displayed. As discussed herein this may be achieved througheach module having a functional and graphical component, the graphicalcomponent being toggled between visible and invisible as required.Alternatively graphical modules may be used to improve the presentationor computation associated with these outputs. For example a scrollingplot 30 may be linked to the basal ganglia 29 to monitor the changingnature of any one or more of the variables. Because a separate module isused more computation regarding the variables plotted, or thepresentation of that plot may be possible. In another graphical output,not shown, the operation or change of the synapse weights may bemodelled or the transmission between modules or neurons inside modulescould be visualised.

Model of Eye Movement

FIG. 10 shows a system which controls eye movement of an avatardisplayed at a screen and which receives stimulus for the avatar at acamera. The camera communicates with a computer vision library whichcommunicates with a face detection unit. The network has the followingmodules: SC: Superior Colliculus; Tr: Trigger Neuron; EBN: ExcitatoryBurst Neuron; LLBN: Long Lead Burst Neuron; OPN: OmniPause Neuron; MN:Oculomotor Neuron; Physics: Physics based Dynamic Contraints. Themodules interact to forma biological type system based on theconnections between them.

FIG. 11 shows an expanded system or model having example emotionalreaction modules which react to the presence of faces (FFA) and modellevels of Corticotrophin Releasing Factor (CRH), B-Endorphin (BE) andOxytocin PHYSIC (OXY). The OXY parameter is a neurotransmitter which isable to change or affect the performance or operation of modules, suchas the CRH. For instance a higher OXY value allows a greater inhibitionof CRH, which lowers stress and the chance of triggering activation ofdistress behavioural circuit which activates PL. The additional modulesare: Face Rec. Face Recognition module; CRH (Corticotrophin ReleasingHormone); BE (Bete Endorphin); OXY (Oxytocin); FFA (fusiform Face area);ZM Zygomatic Major Muscle; and PL Platysmus Muscle.

The adjustment of a parameter, for instance the level of aneurotransmitter such as oxytocin is shown in FIG. 12. The parameter maydefine an aspect of the functional operation of a module. In this casethe responses of the digital entity or avatar and/or characteristicresponses of the digital entity are adjusted by adjustment of aparameter associated with a functional module. In this way thecharacteristics may be changed in an overall sense instead of requiringa series of changes in each module. For instance in some embodiments theadjustment may propagate, such as radially from a module or point in thespace in which the network is defined or linearly across the network oralong connections or through the required structure. In the case of thenetwork of FIG. 12 the OXY parameter is reduced to 50%. If this reflectsthe biological oxytocin system the system dynamics are modified and thesystem becomes more prone to effects of stress, reducing delay inactivation of distress circuits activating the platysmus muscle.

FIG. 13 includes an additional external input or stimuli provided by amicrophone and voice recognition module for multimodal recognition offace and voice. The multimodal recogniser, R, fires if face and voicerecognition is simultaneous. The time both signals arrive at R maydepend on the required structure or connections or the differentprocessing pathways. In this example a time delay of 50 ms in a moduleor connection ensures appropriate signalling times. In an alternativeembodiment a delay may be used to ensure that the signals reach a moduleat the same, or an appropriate time step. FIG. 14 adjusts the delay inthe voice recognition connection, which affects the relative timing ofarrival of voice and face recognition signals. In this example an extradelay has been added compared to the network of FIG. 13.

Development of Eye Movement Model

FIG. 15 shows a schematic of a model taking visual/audial inputs andproducing an output animation. The embodiment of FIG. 15 shows that acomplex biological based architecture can be constructed with a highlevel of model sophistication and the ability to increase functionalityfurther if or when required. A portion of the required structure isshown in which module groups are formed, these module groups may beturned into modules in some embodiments. In a full model the requiredstructure may be more extensive fitting each of the module groups into ahierarchy or other structure. The structure of the model allows theinterconnection between the modules to be incorporated after they arebuilt, providing additional complexity as the model develops. Althoughthis example describes a facial recognition system the model is notlimited to this and the facial recognition system may be only a portionof a complete model.

Considering first an initial system which comprises only the computervision, oculomotor, physics, facial animation render and screen modules.The additional portions of the model may be built from this base.Computer vision input, for example from a camera, is fed to a computervision library which is used to detect the face. The computer visioninput also demonstrates how ‘black box’ functionality or strictlycomputational elements can be integrated with a biologically basedsystem. The detection of a face, or similar limitation or concentrationof the input field (in this case visual) reduces the input datacomplexity for the model. This creates a target which is sent to theSuperior Colliculus (SC) module which generates a saccade (fast eyemovement) which generates activity in the motor neuron which creates anacceleration of the eye.

The physics system dampens the motion with inertial constraints, and theactual movement of the eye is fed back to the SC for correction. Thephysics module assists to reduce the possibility of unnatural movement,for instance by limiting the speed of the response, and to applyphysical constraints. The computed movement of the eye is fed to thefacial animation system which rotates the eye geometry accordingly andrenders it on the screen. The eye movement can be fed back to thecomputer vision system to create a foveal area or line of sight; thisallows the input to be related to an output of the model, creating afeedback loop or dependence in the model.

A more complex model may also include expression detection andexpressive reaction. For instance the visual cortex, limbic system andBrainstem PG may be added into the model. The fusiform face area may bea convolutional neural network (CNN) for facial expression recognitionwhich triggers different emotional behaviours (e.g. fear through thePlatysma or smile through the Zygomatic Major). Central patterngenerators (CPGs) may be used as a basis for the required actions inresponse to the emotions. The facial nucleus resolves facial muscleactivity and sends animation weights to the facial animation systemwhich deforms the geometry and this is fed to the face renderer and thenthe screen.

Neurotransmitters/neuromodulators may also be incorporated into thesystem through their relationship with the amygdala (AMG). The amygdalahas connections (these may relate to biological projections in thiscase) to the autonomic and endocrine systems (e.g. through theHypothalamus). Oxytocin (OXY), corticotrophin releasing hormone (CRH)and beta endorphin (BE) levels have mutual regulatory effects and areused in this example to modulate the triggering of brainstem facialcircuits. The brainstem CPGs create patterns which control facialmuscles over time. The dopamine producing ventral tegmental area (VTA)has anatomic connections from the SC and the AMG, and provides anexample of interconnecting separate neural systems. The ability toconnect modules separately from the configuration of the modules allowsaddition to and modification of the model in a straightforward manner.

Further inputs may be included to the system, for instance an audioprocessing system. This may detect speech. The incorporation of a newmodule may also require new modules in other module blocks, such as thecortex expanding to include a multimodal integration component to blendor combine the audial and visual inputs. However, the addition of a newmodule does not necessarily require the modification of previousconnections, simplifying the expansion of the model. Further addition tothe model may be achieved by the coordination of eye blinks between theoculomotor system and the facial nucleus. Eye blinks (which involve thepalpebral part of the orbicularis oculi muscle (OOc)) are coordinatedwith saccades.

A blink neurons module is added to control the OOc muscle, and timing iscoordinated through a connection with the oculomotor system. A secondstep may introduce a connection form the blink neurons to the dopaminesystem. Spontaneous eye blink rates (EBR) have been shown to be aclinical marker of dopaminergic functioning. A modulatory dopaminergicconnection is made from the VTA to the blink neurons. The dottedconnection indicates how dopamine can modulate the blink rate by addingnew connections between separate systems. This modulates blink ratewhile still coordinating with saccadic activity, illustrating theflexibility of connecting different neural subsystems using the model.While the modules form a required structure comprising, for instance, aseries of module groups or associated modules links can be made withinor outside of these groups by connections. These connections allow theincorporation of high level effects or the coordination of effectsacross different module groups.

Further Structure

Referring again to FIG. 5 the system is shown having a hierarchicalstructure with modules often contained within other modules. In anembodiment of the system an individual module may be referenced byreferring down the tree, from the top module to the base module. Forinstance, the FFA shader could be referenced as Head/brain/FFA/shader.Alternatively it may be preferable in some instances to have inputs andoutputs tied to modules higher in the hierarchy, for instance a commonlyused output such as face 56 may be assigned to the visual system moduleas an available output so as to make referencing the face easier.

Referring now to FIG. 7 a representation of a module 100 is shown. Themodule 100 contains a data element 101 related to the computation 103, adata element 102 related to the graphics 104, an element related to thetransformation of the module 107. Any dependent parts of the module maybe contained in the module 100 but are preferably contained by way of ahierarchical tree structure in which the module is contained. A treestructure has a central point or module from which a plurality ofmodules branch from, with each lower layer of modules capable of havingfurther child modules. In some cases a tree structure may haveadditional modules outside of the main branches. The inputs 109 andoutputs 108 of the module 100 may be variables involved in any one ormore of the elements or the module dependencies. The graphic element ordata 102 this may contain a series of modes 105 associated with actionsof the module and a series of shaders 106 which produce the appropriatelevels of light in the image. Alternatively the module may provide agraphical output visualising a portion of the computation. Thecomputation element may contain instructions, or a pointer to acomputational block contained in a library structure or similar. In somecase the computation element may be limited, the module acting as aconstant or container module to improve the dependency structure of thehierarchy. In other embodiments the computation element may comprise alarge and complex neural network or a single neuron.

The transformation element may provide data regulating how the modulegraphics can change as part of the animation, or how changes todependent structures affect the graphic element. This is of particularimportance when the hierarchical structure is used to traverse themodel. Each module may have a transformation portion which providesinstructions for how to react to changes in the modules above in thehierarchy. For example if the face changes direction the features of theface and the features contained in the brain should also rotate. Therotation of the face will affect the rotation of the eye, which mayaffect the appropriate rotation of the pupil. The hierarchical structureprovides means for these changes to be consistent, so when drawing theelement the changes of an element can be appropriately combined with thesurrounding elements so as to create a realistic animation. Although thedescription of the transformation has been based on the hierarchicalstructure of the model it should be understood that an alternativestructural method may be used which links the transformational means ina different way having as similar outcome.

Architecture

The system structure may comprise a first and a second modelsub-structure (data structure) wherein the first sub-structure (level)is defined by the arrangement of a plurality of computational modulesand the second sub-structure (level) is defined by the connectorslinking the module variables. The first sub-structure may be a scenegraph which is directed and graphical. This may allow the carefularrangement of the modules. The second sub-structure may be a directedgraph in which the connections form edges and the modules form verticesor nodes. These two levels of sub-structure increase the effectivenessof operating the model because the data is separated from thecontrolling code. Therefore the modelling process becomes a method oflinking the plurality of modules (this may be through the use of modulevariables) from the first sub-structure using the second sub-structure,rather than building a completely linked system or designing a processflow. The structure also allows for variables or constants to be updatedwhile the model is operating. This is because the model does not need tobe recompiled as the relationships or connections are separate from thedata.

The first sub-structure may be implemented as a plurality of modules ora structure in which the plurality of modules are organised. The secondsub-structure may be implemented as a set of instructions for combiningmodules. In some embodiments the set of instructions may be located in aplurality of separate files. The separate files may each define aportion or subsection of the connections of the model. In a particularembodiment the instructions may be located in the same structure as, butseparate from, the modules.

First Sub-Structure (Modules)

The first level may comprise an organised structure of the plurality ofmodules. In some embodiments this may be a tree-type structure in whichthe plurality of modules are organised substantially hierarchically. Theplurality of modules may be arranged in a directory-like folderstructure. This is particularly useful when container modules arepresent. FIG. 3 shows a possible structure in which a container module‘scene’ holds a number of modules including a container module ‘face’.The module ‘face’ holds two further modules ‘eyes’ and ‘mouth’. Thiscould be stored in a file like structure in which ‘scene’ was the toplevel folder, ‘face’ and ‘head’ were first level sub folders and ‘eyes’and ‘mouth’ were sub-sub folders and so on. In this way the modelstructure is clear and easily viewable. Module elements may be copied orreplicated by copying the required level of folder and all containedfolders. This may be useful, for example, if each eye was to beindependent. The same model structure would be replicated, however eacheye could have different control signals or small changes could be made.

Second Sub-Structure (Connectors)

The substructure comprises a series of instructions relating to themodules. The instructions may be contained in a single file relating tothe entire model or animation. In a preferred embodiment the secondsubstructure comprises a series of separate, linked files. In anembodiment the instruction files are contained in the same structure asthe modules. They are contained at a hierarchical level one (or more)above all of the models that depend on them in the required structure.For instance instructions linking the ‘eyes’ module may preferably be inthe folder containing the ‘face’ module. However, the instructions couldalso be placed in a folder containing the ‘scene’ module or at any levelabove the ‘face’ module.

It is advantageous to place the instructions in the level directly abovethe module they refer to as this provides an efficient modellingtechnique. In particular, if changes need to be made to a certainmodule, or its instructions, the correct location can be found simply.Secondly the collocation of a module and related instructions allows theentire module to be quickly replicated with appropriate instructions.This may be useful so as to move the module to a different model or tocopy the module internally within the model. In an embodiment there areseparate instructions at each stage of the first substructure, so that:

-   -   instructions for ‘direction’ are in the eyes folder,    -   instructions for ‘eyes’ are in the ‘face’ folder, and    -   instructions for the ‘face’ are in the ‘scene’ folder.        Operation

When a model runs it compiles the first and second sub-structures(preferably arranged in a directory tree as described above) containingthe configuration files to create its modules, connectors, geometry etc.A required structure may be in the form of a directory tree that may bevaried in structure but is able to build the plurality of modules andthe links between them. At each time step the structure must betraversed and updated. This may proceed either from a bottom up or topdown approach, dependent on the particular design of the model asdescribed above but is preferably top-down in a hierarchical structurewith the head at the top. Each module is evaluated based on the inputscurrently provided. This includes all container modules and theirchildren. If a module has no code, such as a container module then nochange will occur. If however, code or computational material is presentthis will be run, and is typically independent of any other part of thesystem. The results of the time step are then transmitted to the outputfields. In a second pass through the structure the outputs may then becopied across the connections. This updates each of the modules inputsfor the next time step. In some instances there may be processing whichtakes place on the connections, for instance holds or thresholds whichmay be updated in one or both of the stages. If substantial changesappear, or a set time period has passed the model may rebuildcompletely, including substantially all elements to ensure continuity.

In a particular example, as shown in FIG. 4, files or folders withequivalent names (not including any file extension) are assumed tocontain data belonging to the same object. For instance, geometry data(.obj, .frag, .vert, .geom, .mtl, .tex, .trans, or image files) musthave an equivalent name to the object it belongs to and should be placedinside a folder of an equivalent name. Object definition files, orinstruction files (.blm, .blc) may be placed in the same parentdirectory as this folder. A simple module with geometry, shaders but notextures could be specified as shown in FIG. 4. Therefore when the codeis operating it may read and connect items with common names and theseprovide the additional details to the model.

Modules

If a new module is required this can be prepared separately of themodelling system. Modules may vary depending on the embodiment andparticular model but may include:

-   -   Animation;        -   provide a known time step,        -   includes an animation file,    -   Folder modules;        -   also known as a container modules,        -   Hold other modules,    -   Neuron module;        -   E.g. leaky integrate and fire module,        -   Multiple leaky integrate neurons,    -   Synapse weights module;        -   May be combined with neurons module to form self-contained            artificial neural network,    -   Visual interface modules;        -   Scrolling display module to illustrate outputs,    -   Interface modules;        -   Vision module,        -   May control interactions with outside world e.g. camera or            microphone,    -   A constant value;        -   no dependence on time-stepping,    -   Black box;        -   Stand-in module to perform task or to be updated later,    -   Nothing;        -   Empty modules may be ignored.

Further modules or types of module may be created as required.

Module Descriptions

Before a module can be used in the modelling environment it must firstbe created. This involves defining the inputs and outputs of the modeland the relations between them. The module definitions are then placed.

For instance, considering a well-known model of a neuron, such as theleaky integrate and fire neuron, this may be described mathematicallyby:

${{I(t)} - \frac{{V_{m}(t)} - V_{m_{tonic}}}{R_{m}}} = {C_{m}\frac{{dV}_{m}(t)}{dt}}$Which is conveniently rewritten as:

$\frac{{dV}_{m}(t)}{dt} = {{{FC}_{i} \cdot {V_{i}(t)}} - {{FC}_{m}\left( {{V_{m}(t)} - V_{m_{tonic}}} \right)}}$

The module definition lists the variables and describes the action ofthe module when used. The variables are important because they allowconnections or links to be made between modules. Variables are how amodule's parameters can be accessed in order to make links orconnections. Some variables may refer to multiple values while some mayrefer to only a single value. Some variables are designated as outputvariables. These variables are the output of a module's computationalprocesses and are defined so as they may not be modified externally,being effectively “read-only” variables. Other variables are designatedas input variables. These variables affect the module's computation ofits outputs, but are not themselves changed by the module. They are“read-write” variables able to be modified externally or simply read.Occasionally a variable may be designated both input and output. Thismeans that it may be externally modified, but that its value may also bemodified as a result of the module's computation.

Every module type except for container modules may require a file to setits type and its parameters. The first line of this file typicallyidentifies the type of module it's creating. The following lines containsettings relevant to the type specified on the first line. In aparticular model or animation the module file (for instance .blm) maycreate an object based on one of the defined modules such as the leakyintegrate and fire neuron. A module is inserted into the model bydefining the required inputs and outputs. An example code for the moduleis shown in code section A. Ignoring the formatting, this example modulefirst names the module type and then lists each input followed by adefault value. If an input is not declared when inserting a module thedefault value may be used. In this way the neuron model must only becreated once and then may be inserted into the animation or model atmultiple points through creation of an appropriate .blm file.

Code Section A BL_leaky_integrate_and_fire_modulenumber_of_inputs=<number_of_inputs> [voltage=<starting voltage>[=0.]][fired=<starting_fired_value>[=0.]] [fired_value=<fired_value>[=0.]][firing_threshold_voltage=<threshold_voltage>[=0.]]input_frequency_constants=<input_frequency_constants>[=0.][input_voltages=<input_voltages>[=0.]][maximum_voltage=<maximum_voltages>[=0.]][membrane_frequency_constant=membrane_frequency_constant>[=0.]][minimum_voltage=<minimum_voltage>[=1.]][reset_voltage=<reset_voltage>[=0.]][tonic_voltage=<tonic_voltage>[=0.]] [use_firing=<use_firing>[=0]][time_step=<time_step>[=0. 001]]Variables and Connections

Variables and connectors provide links between the plurality of modulesof the first sub-structure. Variables provide means for a module'sparameters to be accessed by connectors. Some variables may refer tomultiple values while some may refer to only a single value. Variablesmay be defined as internally or externally available and editable ifrequired. Modules may have several variables, which may be either inputor output (sometimes both). Output variables are determined by thecomputational processes of their owner module and may be read, but notmodified, by connectors. Input variables are input parameters to themodule's computation and may be both read and written by connectors.When referring to a variable (or any module or connector for thatmatter), the syntax reflects the hierarchical, directory-based structureof the data.

The variables may be created as part of the module building definitionprocess. Variables can be linked together by instructions to create themodel or animation. Instructions link one, or a plurality of variables,so that in a time step variables may be passed between modules. In someembodiments variables may also have holds, pauses or other processingwhen being passed between modules or the timing may be otherwiseadjusted. In some embodiments variables may have sub-variable members.This provides a means to refer to a group of variables or a member ofthe group. For instance, a file named texture data may have threesub-variables:

-   -   texture.data—referring to the texture's colour data array;    -   texture.width—referring to the texture's width (in texels); and    -   texture.height—referring to the texture's height (in texels).

To refer to a module, the directory path to the module, beginning at thedirectory in which the reference is made may be used. For example if themodule “test_module” is located in the same directory as a connector,the module is simply called “test_module”. However, if test_module is achild of a module called “parent_module” and connector is in the samedirectory as parent_module, then “parent_module/test_module” is used.Variables may be considered as children of their parent modules and arereferred to using the same hierarchical syntax. If test_module has avariable called “output”, this variable is referred to as“test_module/output”. The same rules about directory paths describedabove may be applied. To refer to the variable output when in theparent_module directory (see previous paragraph), it is necessary to usethe path “parent_module/test_module/output”. It may be observed thatinstructions contained in files near to their associated modules providesimple names in such an embodiment.

Connectors

The second sub-structure links or connects modules together. Connectorslink variables of modules to one another. Files which define connectorsmay include an identifier declaring the type of connector to create,preferably on the first line. The connector files also containtype-specific information, although generally there will be at least oneline in which the input variables are transmitted to a, or a pluralityof, other variable.

Possible connector types include, but are not limited to

-   -   Identity Connectors        -   Strict equalities        -   Simple and common

BL_identity_connectorsimple_module/input_variables[0]=another_module/output_variables[2]another_module/special_variable=a_third_module/output_variables[0]

-   -   Linear Transform Connector        -   Transforms variable when transmitting        -   Threshold based relationships        -   Combinations of variables        -   Comparisons of variables    -   Damped sum connectors        -   a system of linear transformation connectors and neurons            such as leaky-integrate-and-fire (LIF). Connects linear            combinations of inputs to output variables but “damps” the            sum of these inputs by passing them through a LIF neuron            first.

Where in the foregoing description, reference has been made to specificcomponents or integers of the invention having known equivalents thensuch equivalents are herein incorporated as if individually set forth.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise”, “comprising”, and thelike, are to be construed in an inclusive sense as opposed to anexclusive or exhaustive sense, that is to say, in the sense of“including but not limited to”.

Although this invention has been described by way of example and withreference to possible embodiments thereof, it is to be understood thatmodifications or improvements may be made thereto without departing fromthe scope or spirit of the invention as defined in the appended claims.

What we claim is:
 1. A computer implemented system for generatinginteractive behavior of an embodied virtual character or digital entity,the system comprising: a plurality of interconnected modules togetherrepresentative of a neurobehavioral model, at least one of the pluralityof interconnected modules receiving data characterizing a real-worldinput stimulus; wherein the neurobehavioral model is time-stepped suchthat the plurality of interconnected modules operate in a time-step toprovide an external output defining a response of the virtual characteror digital entity to the external stimulus and wherein time-steppingwithin modules occurs at a rate providing multiple computationaltime-steps before a response is provided.
 2. The system of claim 1,including an external output defining an audible response of the virtualcharacter or digital entity to the external stimulus.
 3. The system ofclaim 1, including an external output defining a physical response ofthe virtual character or digital entity to the external stimulus andwherein the physical response is any of: change of orientation, a changeof emotional expression, a change in body language, or mirroring theexternal input.
 4. The system of claim 1, wherein each module has atleast one variable and is associated with at least one connector,wherein the connectors communicate information on variable valuesbetween modules in a time-stepped manner.
 5. A computer implementedsystem for generating interactive behavior of an embodied virtualcharacter or digital entity, the system comprising: a plurality ofinterconnected modules together representative of a neurobehavioralmodel, at least one of the plurality of interconnected modules receivingdata characterizing a real-world input stimulus; wherein theneurobehavioral model is time-stepped such that the plurality ofinterconnected modules operate in a time-step to provide an externaloutput defining a response of the virtual character or digital entity tothe external stimulus and wherein the plurality of the modules havecoupled computational and graphical elements, each module representing abiological process and having a computational element relating to andsimulating the biological process and a graphical element visualizingthe biological process.
 6. A computer implemented system for generatinginteractive behavior of an embodied virtual character or digital entity,the system comprising: a plurality of interconnected modules togetherrepresentative of a neurobehavioral model, at least one of the pluralityof interconnected modules receiving data characterizing a real-worldinput stimulus; wherein the neurobehavioral model is time-stepped suchthat the plurality of interconnected modules operate in a time-step toprovide an external output defining a response of the virtual characteror digital entity to the external stimulus and wherein the plurality ofinterconnected modules together representative of a neurobehavioralmodel simulate multiple parallel systems for generating behavior, and atleast one module is configured to resolve competing inputs from aplurality of other inputs from the interconnected module to provide theexternal output defining a response of the virtual character or digitalentity to the external stimulus.
 7. A facial graphics rendering system,comprising: a plurality of layers, the plurality of layers comprising: agraphics rendering layer which receives muscle actuation/position datadefining degrees of actuation of a set of facial animation muscles andwhich generates graphics image data; a muscle actuation/integrationlayer receiving nerve actuation data defining a degrees of nerveactivation for a set of animation nerves and generating muscle actuationdata for a set of activation muscles defined for the muscle actuationlayer; and a nerve activation layer receiving expression data definingan expression and generating nerve activation data defining acombination of animation nerves to be activated and defining a degree ofactivation for each nerve.
 8. The system of claim 7, wherein each of theplurality of layers receives stimulus data and generates feedback data.9. The system of claim 5, wherein at least one of the plurality ofinterconnected modules receives data characterizing an internal stimulusfrom another one of the plurality of interconnected modules, wherein theinternal stimulus also affects the external output defining the responseof the virtual character or digital entity to the external stimulus. 10.The system of claim 5, wherein the real-world stimulus is received fromone or more of the group consisting of a camera, an electromagnetictransducer, an audio transducer, a keyboard, and a graphical userinterface.
 11. The system of claim 5, wherein the interconnected modulesare configured to exhibit chaotic behavior, depending in a nonlinear wayon a past state as characterized by module variable values.
 12. Thesystem of claim 5, wherein the output is at least one of: graphicaloutput or actuation of a physical robot.
 13. The system of claim 5,wherein at least one of the plurality of interconnected modules createsan association between the external stimulus and the external output.14. The system of claim 5, including an external output defining anaudible response of the virtual character or digital entity to theexternal stimulus.
 15. The system of claim 5, including an externaloutput defining a physical response of the virtual character or digitalentity to the external stimulus and wherein the physical response is anyof: change of orientation, a change of emotional expression, a change inbody language, or mirroring the external input.
 16. The system of claim5, wherein the external stimulus affects the operation of theneurobehavioral model over time.
 17. The system of claim 5, wherein eachmodule has at least one variable and is associated with at least oneconnector, wherein the connectors communicate information on variablevalues between modules in a time-stepped manner.
 18. The system of claim6, wherein at least one of the plurality of interconnected modulesreceives data characterizing an internal stimulus from another one ofthe plurality of interconnected modules, wherein the internal stimulusalso affects the external output defining the response of the virtualcharacter or digital entity to the external stimulus.
 19. The system ofclaim 6, wherein the real-world stimulus is received from one or more ofthe group consisting of a camera, an electromagnetic transducer, anaudio transducer, a keyboard, and a graphical user interface.
 20. Thesystem of claim 6, wherein the interconnected modules are configured toexhibit chaotic behavior, depending in a nonlinear way on a past stateas characterized by module variable values.
 21. The system of claim 6,wherein the output is at least one of: graphical output or actuation ofa physical robot.
 22. The system of claim 6, wherein at least one of theplurality of interconnected modules creates an association between theexternal stimulus and the external output.
 23. The system of claim 6,including an external output defining an audible response of the virtualcharacter or digital entity to the external stimulus.
 24. The system ofclaim 6, including an external output defining an physical response ofthe virtual character or digital entity to the external stimulus andwherein the physical response is any of: change of orientation, a changeof emotional expression, a change in body language, or mirroring theexternal input.
 25. The system of claim 6, wherein the external stimulusaffects the operation of the neurobehavioral model over time.
 26. Thesystem of claim 6, wherein each module has at least one variable and isassociated with at least one connector, wherein the connectorscommunicate information on variable values between modules in atime-stepped manner.